appropriate environment to enhance your contacts and to establish new ones. The conference is a ... Mines Area using Geophysical and Geostatistical Methods.
Short Abstracts
IAMG2018 Olomouc, Czech Republic, September 2-8, 2018, The 19th Annual Conference of the International Association for Mathematical Geosciences
Editors: Karel Hron Ondřej Bábek Eva Fišerová Regina van den Boogaart
Contents
Preface Dear Friends and Colleagues, We warmly welcome you to Olomouc, for the Nineteenth Annual Conference of the International Association for Mathematical Geosciences (IAMG2018). As many of you know, this year the IAMG celebrates its 50th anniversary. In addition to the main scientific aim of the conference, this occasion will be proudly celebrated with all those who have contributed to scientific growth of IAMG in the last decades. The leading topic of the conference is “Tools for Data Analysis in Geosciences”. Accordingly, IAMG2018 aims at bringing together researchers and practitioners to discuss recent developments regarding the applications of mathematics, statistics, computer science and informatics in geosciences. The IAMG2018 programme consists of 6 plenary talks and almost 200 oral and poster contributions. The co-chairs have done their best to provide a balanced and stimulating programme that will appeal to the diverse interests of the participants. The international organizing committee hopes that the conference venue will provide the appropriate environment to enhance your contacts and to establish new ones. The conference is a collective effort by many individuals who organized it with heart and soul. The Scientific Committee, the Topical Session Organizers, the hosting faculties and many volunteers have contributed substantially to the organization of the conference. We acknowledge their work and the support of Palacký University Olomouc, Czech Republic. On the occasion of IAMG2018, special issues are planned for all three IAMG journals: Mathematical Geosciences, Computers & Geosciences and Natural Resources Research, covering topics of the IAMG2018 conference. The latter two calls are exclusively for IAMG2018 participants. Deadline for submissions to all journals is October 31, 2018. We would like to encourage you to submit the best from your research work there! Looking forward, the IAMG2019 will be held at the Pennsylvania State University, United States of America, from Saturday the 10th to Friday the 16th of August 2019. You are invited and encouraged to actively participate in this event. We wish you a productive, stimulating conference and a memorable stay in Olomouc. Karel Hron and Ondřej Bábek, IAMG2018 Chairs
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Contents G00. Plenary Talks G0001. Mathematics overcame the statistical problems of closure: Has it solved real-world geochemical problems? Mark A. Engle, Nicholas E. Pingitore . . . . . . . . . . . . . . . . . . . G0002. Multiple-point geostatistics: when do they work, when do they not work Gregoire Mariethoz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G0003. Spatial decorrelation methods revisited Ute Mueller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G0004. The High-order Stochastic Sequential Simulation Framework: A review with examples Roussos Dimitrakopoulos . . . . . . . . . . . . . . . . . . . . . . . . . . G0005. Some aspects of geostatistical simulations Christian Lantuéjoul . . . . . . . . . . . . . . . . . . . . . . . . . . . . G0006. Geological objects and physical parameter fields in the subsurface: a review Guillaume Caumon . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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G01. 3D/4D Geomodeling G0101. 3D geological model meshing method based on corner-point grid and First-sedimentary-Last-structure approach Xuechao Wu, Gang Liu, Qiyu Chen, Yang Li . . . . . . . . . . . . . . . G0102. 3D modelling and visualization of the resources warehouse of mineral rock specimens Chen Zhijun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G0103. A graph-based approach to simplify subsurface structural models and assess the impact on physical modeling Pierre Anquez, Guillaume Caumon, Jeanne Pellerin . . . . . . . . . . . G0104. Automatic strata comparison method of urban geological section for 3D modelling based on knowledge graph Gang Liu, Qiyu Chen, Fonan Zhong, Jiyin Zhang, Xuechao Wu . . . . . . G0105. Geophysical forward modelling with GECCO tools for heterogeneous lithological associations – the closed Mullikkoräme massive volcanic zinc sulphide mine in Finland as a case study
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Eevaliisa Laine, Johan Ersfolk, Ilkka Suppala, Marit Wennerström, Jan Westerholm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
G0106. Geostatistical Facies Modelling of Cyclicity, Rhythmicity and Diagenesis in Sedimentary Sequences Thomas Le Blevec, Olivier Dubrule, Cédric M. John, Gary J. Hampson . . G0107. Multi-Physics Joint Inversion - a Flexible Expert-distributed Approach Peter L. Smilde, Christina Mueller, Markus H. Krieger, Soegun Petersen .
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Contents G0108. Plurigaussian Simulation of Geological Domains in the Presence of Spatial Trends Nasser Madani, Xavier Emery . . . . . . . . . . . . . . . . . . . . . . . G0109. Resources Modelling of Alluvial Tin Deposit in Former Artisanal Mines Area using Geophysical and Geostatistical Methods Mohamad Nur Heriawan, Guntan Viliarso Seran, Olga Padmasari Anggraini, Wahyudi Widyatmoko Parnadi . . . . . . . . . . . . . . . . .
G02. Compositional Data Analysis G0201. 3D soil texture mapping with L1-regularized multinomial logistic regression Milutin Pejović, Mladen Nikolić, Branislav Bajat . . . . . . . . . . . . . G0202. Block cokriging and the flow anamorphosis Raimon Tolosana-Delgado, K. Gerald van den Boogaart, Ute Mueller . . . G0203. Decoupling processes from soil geochemistry: Mapping surficial/bedrock geochemical signatures in Northern Ireland Eric Grunsky, Jennifer McKinley, Ute Mueller . . . . . . . . . . . . . . G0204. Exploration targeting by multivariate compositional extrema
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G0205. How can we use compositional data for determining threshold values for environmental health assessment? Jennifer McKinley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G03. Data Assimilation and Data Integration G0301. An Improved Method For Fracture Modeling Based On Principle Component Analysis And PR Model Shuang Sun, Jiagen Hou, Yuming Liu, Suihong Song . . . . . . . . . . . G0302. Data normalization approach in geological favorability about shale oil exploration for Shahejie Formation in the Liaohe Depression, Bohai Bay Basin, China Jingdu Yu, Man Zheng, Qiulin Guo . . . . . . . . . . . . . . . . . . . . G0303. Data-driven fusion of multi-resolution digital elevation models and remote sensing imagery Luiz Gustavo Rasera, Gregoire Mariethoz, Stuart N. Lane . . . . . . . . . G0304. Distributional Data Assimilation for Resource Model Updating Angel Prior Arce, Raimon Tolosana-Delgado, K. Gerald van den Boogaart, Joerg Benndorf, Alessandra Menafoglio . . . . . . . . . . . . . . . . . .
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G0305. Importance of Initial Ensemble Design for Reliable History Matching – Comparison of Ensemble-based Methods Applied to the 3D Egg Model Byeongcheol Kang, Junyi Kim, Kyungbook Lee, Hoonyoung Jeong, Jonggeun Choe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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G0306. Localization of Sulfide Mineralization using 2D Electrical Resistivity Tomography and Enhanced Local Wave number techniques over Bouguer Gravity anomaly Ashok Kumar Gupta, Roshan k Singh, Shalivahan Srivastava, Shovana Mondal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
G04. Fractal and Multi-Fractal Modelling, Singularity Analysis G0401. Fractal density and local singularity analysis - now linear mathematical geosciences theory and method for Modeling Extreme GeoEvents Qiuming Cheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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IAMG2018 - Short Abstracts G0402. Geochemical anomaly uncertainty assessment based on stochastic simulation and local singularity analysis Yue Liu, Kefa Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G0403. Lithogeochemical anomaly prospectivity mapping of HREEs using multi-fractal modeling in Saghand Area, Central Iran Masoumeh Khalajmasoumi, Behnam Sadeghi . . . . . . . . . . . . . . . G0404. Multi-fractal modeling: a significantly useful method to recognize geochemical anomalies in large-scale sampling networks Fabrizzio Sánchez, Behnam Sadeghi . . . . . . . . . . . . . . . . . . . . G0405. Multifractal Studies Using Magnitude Cumulant Analysis of Wavelet Transform: Application to Ionospheric Total Electron Content Data Shivam Bhardwaj, Enamundram Chandrasekhar, Vikram M. Gadre . . . .
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G05. Functional Data Analysis G0501. A novel inferential framework for the analysis of spatio-temporal geochemical data Veronika Římalová, Alessandra Menafoglio, Alessia Pini, Eva Fišerová . . G0502. Graph-based Spatio-temporal Clustering Using Stochastic Tree Partitioning in a Functional Data Framework Orhun Aydin, Mark Janikas, Kevin Butler . . . . . . . . . . . . . . . . . G0503. Underground Mining to Processing Copper Ore Tracking Solution in DISIRE Project Leszek Jurdziak, Witold Kawalec, Robert Król . . . . . . . . . . . . . . .
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G06. Geoinformatics G0601. A GIS-based approach to identify optimum locations of wind power plants using a multi-criteria model applied to Afghanistan Abdul Saboor Hamza, Jan C. Bongaerts, Helmut Schaeben . . . . . . . . G0602. Hot spot analysis of environmental variables in the big data era Chaosheng Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G0603. Towards a web-based information system for multi-physics detector data
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Florian Bachmann, Mario Hopfner, Heinrich Jasper, Helmut Schaeben, Björn Wieczoreck . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
G07. Geostatistics G0701. A Pareto Approach to the Construction of an Optimal Spacefilling Design Christien Thiart, Kago Kebotsamang, Linda Haines . . . . . . . . . . . . G0702. Anisotropic Kernel Function based Geographically Weighted Regression for Mineral Exploration Jie Zhao, Wenlei Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . G0703. Building a continue spatial variation grid from an isovalue map Jean-Michel Metivier . . . . . . . . . . . . . . . . . . . . . . . . . . . . G0704. Geostatistics for Geometallurgical Property Prediction K. Gerald van den Boogaart, Peter Menzel, Kai Bachmann, Nataliia Krupko, Angel Prior, Raimon Tolosana-Delgado, Jens Gutzmer . . . . . .
G0705. High-Order, Data-Driven Categorical Simulation and Applications to Mineral Deposits Ilnur Minniakhmetov, Roussos Dimitrakopoulos . . . . . . . . . . . . . . G0706. Three-dimensional stochastic modeling framework for Quaternary sedimentary structures using multiple-point statistics Qiyu Chen, Gang Liu, Gregoire Mariethoz, Xiaogang Ma . . . . . . . . .
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Contents G0708. Probabilistic assessment of in-place coal tonnage for public disclosure of mineral resources Ricardo A. Olea, Jon E. Haacke, Brian N. Shaffer, James A. Luppens . . G0709. Quantile Sampling: a new approach for multiple-point statistics simulation Mathieu Gravey, Gregoire Mariethoz . . . . . . . . . . . . . . . . . . . . G0710. Simulation of the ore boundaries of a lateritic bauxite deposit using multiple-point statistics Yasin Dagasan, Philippe Renard, Julien Straubhaar, Oktay Erten, Erkan Topal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
G0711. Spatial estimation of daily rainfall over a number of years with small data sets Peter Dowd, Eulogio Pardo-Igúzquiza . . . . . . . . . . . . . . . . . . . G0712. Kriging for tensor data through Object Oriented Spatial Statistics Alessandra Menafoglio, Davide Pigoli, Piercesare Secchi . . . . . . . . . . G0713. Virtual mineral processing simulation in software MLALookUP Nataliia Krupko, Marius Kern, K. Gerald van den Boogaart . . . . . . . . G0714. Wall scale analysis of weathering feature distribution across sandstone facades Brian Johnston, Jennifer McKinley, Patricia Warke . . . . . . . . . . . . G08. Machine Learning, Pattern Recognition, Data Mining, Big Data G0801. Prospectivity modeling incorporating spatial dependencies through convolutional neural networks Samuel Kost, Georg Semmler . . . . . . . . . . . . . . . . . . . . . . . G0802. Can boosting boost exploration targeting? Melanie Brandmeier, Irving Cabrera, Vesa Nykänen, Maarit Middleton . . G0803. Fusing traditional and contemporary modelling approach in multiscale mineral potential studies Soile Aatos, Eevaliisa Laine, Mikko Kolehmainen . . . . . . . . . . . . . G0804. A non-destructive measuring method for rock strength via hammering sound Shuai Han, Heng Li, Mingchao Li, Qiubing Ren . . . . . . . . . . . . . . G0805. Reconstruction of vugular carbonate rocks by pore network modeling and image-based network technique Saeid Sadeghnejad, Jeff Gostick . . . . . . . . . . . . . . . . . . . . . . G0806. Comparing linear regression and Gaussian Processes approaches to approximate mineral group densities in an iron ore deposit Mehala Balamurali, Katherine L Silversides, Arman Melkumyan . . . . . G0807. Distributed Indexing Technique for Timelines He Zhenwen, Xiaogang Ma . . . . . . . . . . . . . . . . . . . . . . . . . G0808. Enhancement of unconventional oil and gas production forecasting using mechanistic-statistical modeling Justin B. Montgomery, Francis M. O’Sullivan . . . . . . . . . . . . . . . G0809. Fast and robust probabilistic classification method for fracture identification in BIG seismic datasets Egbadon Udegbe, Eugene C Morgan, Sanjay Srinivasan . . . . . . . . . . G0810. Feature Extraction from Spatial Fields Sean A. McKenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G0811. Classification for Small and Unbalanced Hyperspectral Image Based on Generative Adversarial Networks Kang Wu, Zhaoying Yang, Wang Yao, Jin Qin, Ying Zhan, Ying Cao, Yuntao Wang, Xianchuan Yu . . . . . . . . . . . . . . . . . . . . . . . .
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IAMG2018 - Short Abstracts G0812. Improved Well Placement Optimization Procedure Using Geomechanical Constraints and Machine Learning Gaetan Bardy, Jeffrey Yarus, Shohreh Amini, Harold Walters, Steven Drinovsky . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
G0813. Automatic fission track recognition and measurement in 3D Alexandre Fioravante de Siqueira, Sandro Guedes . . . . . . . . . . . . . G0814. Characterising Measure While Drilling data responses to changes in rock hardness Katherine L Silversides, Arman Melkumyan . . . . . . . . . . . . . . . . G0815. Study of Risk assessment to Linear Engineering Structures due to Thermokarst Processes on the basis of remote sensing and mathematical modeling Veronika Kapralova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P0217. Study on data chains, big data minig and super-computer platformbased intelligent monitoring, simulation, control and early warning of urban soil pollution Yongzhang Zhou, Xiaotong Yu, Fan Xiao . . . . . . . . . . . . . . . . . G09. Numerical Modelling and Numerical Simulation G0901. An application of the restart Ensemble Kalman filter for the identification of contaminant source in a sandbox experiment with uncertainties Zi Chen, J. Jaime Gómez-Hernández, Teng Xu, Andrea Zanini, Fausto Cupola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
G0902. Generating variable shapes of salt geobodies from seismic images Nicolas Clausolles, Pauline Collon, Guillaume Caumon . . . . . . . . . . G0903. Geostatistical Reservoir Characterization to Predict Best Lithofluids discriminators for Lumshiwal Sandstone: A Rock Physics Based Study Nisar Ahmed, Mubasher Ahmad, Perveiz Khalid . . . . . . . . . . . . . . G0904. Mathematical model of erosion and deposition in deformable porous media Eduard Khramchenkov, Maxim Khramchenkov, Denis Demidov . . . . . . G0905. Natural Gas Demand under Multi-Factor Orthogonal Decomposition Method Wei Yan, Yuwen Chang . . . . . . . . . . . . . . . . . . . . . . . . . . G0906. Numerical approach for faster and precise pressure and overpressure analysis in petroleum system modeling Renaud Traby, Mathieu Ducros, Isabelle Faille, Françoise Willien . . . . . G0907. Physical simulations on geological models using unstructured grids
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G0908. Proxy model for hydraulic fracture propagation and seismic wave propagation processes in a fractured reservoir Manik Singh, Sanjay Srinivasan . . . . . . . . . . . . . . . . . . . . . . G0909. Regional gravity field improvement and its application to geophysical modelling in Antarctica
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Theresa Schaller, Mirko Scheinert, Roland Pail, Petro Abrykosov, Philipp Zingerle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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G0910. Reservoir Characterization of Cretaceous Sand to Predict the Pore Fluid heterogeneities by Applying AVO Attributes: A Case Study from Indus Basin, Pakistan Mubasher Ahmad, Nisar Ahmed, Perveiz Khalid . . . . . . . . . . . . . .
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Contents G0911. Testing the hypothesis that variations in atmospheric water vapour are the main cause of fluctuations in global temperatures Ivan Kennedy, Migdat Hodzic . . . . . . . . . . . . . . . . . . . . . . .
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G10. Spatial Statistics G1001. A regression model for crystallographic orientations subject to phase transformation Richard Arnold, Florian Bachmann, Peter E. Jupp, Helmut Schaeben . . G1002. Geochemical Element Combination Anomalies Extraction Based On Spatial Neighborhood Local Correlation Coefficients
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Zhaoying Yang, Kang Wu, Jin Qin, Wang Yao, Ying Zhan, Xianchuan Yu
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G1003. Limitations of spectral analysis of sedimentary proxy records, tested on simulated time series with timescale error and variable temporal resolution István Gábor Hatvani, Péter Tanos, Zoltán Kern . . . . . . . . . . . . . G1004. Linear compositional trend for the frequency of ocean wave events. A Bayesian approach. Maribel Ortego, Jesus Corral-López, Juan José Egozcue, Jan Graffelman . G1005. Manage of High Pressure Salt Water Invasion Between Salt Layers with High Density OBM in Deep Well Jianhua Wang, Wei Zhang, Haijun Yan, Xianguang Xu, Man Zheng . . . G1006. Modelling undiscovered oil resources: A stochastic geometry approach Erik Anderson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G1007. Universal law for waiting internal time in seismicity and its implication to complex network of earthquakes Norikazu Suzuki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G1008. Bayesian Prediction of Spatial Data with Non-Ignorable Missingness Using INLA and SPDE Mohsen Mohammadzadeh, Samira Zahmatkesh . . . . . . . . . . . . . . P01. Poster Session A P0102. A novel approach for characterizing the spatial heterogeneity of coastal morphology - case study at southern Baltic Sea Junjie Deng, Jiaxue Wu, Wenyan Zhang, Joanna Dudzinska-Nowak, Jan Harff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
P0103. A point exchange non-dominated sorting algorithm for Pareto optimal space-filling designs Kago Kebotsamang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P0104. Agent based system for the rangeland management ”application in Djelfa province Algeria” Zerguine Abderrahman, Belhadj Aissa Mostefa . . . . . . . . . . . . . . . P0105. Application of Geostatistical Techniques for the Determining of Anomalous Zones of Copper Ore Deposit Barbara Namysłowska-Wilczyńska . . . . . . . . . . . . . . . . . . . . . P0106. Denoise for Soil Geochemical Data Based on Sparse Representation Wei Youhua, Lin Wu, Xiangquan Zhou, Huan Liu . . . . . . . . . . . . . P0107. Geological modeling of porous carbonate reservoir based on seismic and rock type Mingchuan Wang, Taizhong Duan . . . . . . . . . . . . . . . . . . . . .
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IAMG2018 - Short Abstracts P0108. Heaps of Information – Exploratory Data Analysis of Geophysical and Borehole Data for the Investigation of Tailings at an Abandoned Mining Site using “R” and a 3D-Geodatabase. Heinz Reitner, Christian Benold, Adrian Flores-Orozco, Jakob Gallistl, Alexander Römer, Albert Schedl . . . . . . . . . . . . . . . . . . . . . .
P0109. Identification of the key domains in LGOM copper deposit Krzysztof Holodnik, Wojciech Kaczmarek . . . . . . . . . . . . . . . . . . P0110. Impact of the compositional nature of data on reserve evaluation in a coal deposit, Iran Hossein Molayemat, Farhad Mohammad Torab, Vera Pawlowsky-Glahn, Amin Hossein Morshedy, Juan José Egozcue . . . . . . . . . . . . . . .
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P0112. Partial Grade method to improve estimation of multi-unit deposits with soft boundaries: application to an iron mine deposit Sara Kasmaeeyazdi, Giuseppe Raspa, Chantal de Fouquet, Stefano Bonduà, Francesco Tinti, Roberto Bruno . . . . . . . . . . . . . . . . . . . . . .
P0113. The Application of Invasion Depth Model of Drilling Fluid Particles and Filtrate Jianhua Wang, Wei Zhang, Man Zheng . . . . . . . . . . . . . . . . . . P0114. The Transportation Study of Ore-sourced Elements in the Cover Layer with respect to Particle Grades by Varying Coefficient Models Deyi Xu, Qiuming Cheng, Shuyun Xie . . . . . . . . . . . . . . . . . . . P0115. The influence of image resolution on pore-scale modelling results: a comparison of super-resolution technique and experimental dataset Marina V. Karsanina, Kirill Gerke, Rail I. Kadyrov, Siarhei Khirevich, Timofey O. Sizonenko . . . . . . . . . . . . . . . . . . . . . . . . . . .
P0116. Trapped gas bubbles in sand: Determining their effect on hydraulic conductivity and CT imaging Tomas Princ, Helena MR Fideles, Johannes Koestel, Michal Snehota . . . P0117. Uncertainty in geomodels - a work-package within the GeoERA Project Bjoern Zehner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P0118. Using Sedsim to predict continental-scale coastal response to climate change Cedric Griffiths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P0119. “8C” criteria of data sources for data mining in petroleum industry informatization Li Dawei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P02. Poster Session B P0201. Kohonen neural network and factor analysis applied to identify and extract Ag-Au mineralization Xiaotong Yu, Fan Xiao, Yongzhang Zhou . . . . . . . . . . . . . . . . . P0202. Petrophysical Analysis Using Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Fractured Granite Basement Reservoir in Cuu Long Basin, Vietnam Huy Giao Pham, Nakaret Kano, Kushan Sandunil . . . . . . . . . . . . . P0203. Short and long term regional and global climate modeling using multi scale Kalman Filtering Migdat Hodzic, Ivan Kennedy . . . . . . . . . . . . . . . . . . . . . . . P0204. MPS-based Geological Pattern Reconstruction with 2D Crosssections Hou Weisheng, Tiancheng Zheng, Hengguang Liu . . . . . . . . . . . . . P0205. Variational Autoencoders in new instances generation tasks Gleb Shishaev, Vasily Demyanov, Alexander Mokryak . . . . . . . . . . .
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Contents P0206. Remote Sensing Model Construction of Degree of Rock Weathering in the Nujiang Fault Zone Zhifang Zhao, Shucheng Tan, Qi Chen, Lin Luo, Jing Xi, Runhuai Hong, Haiying Yang, Binxian He . . . . . . . . . . . . . . . . . . . . . . . . .
P0207. Multidomain clustering as a key assistant for history matching Nikita Bukhanov, Gleb Shishaev, Vasily Demyanov, Boris Belozerov . . . P0208. Forward stratigraphic modeling of subduction-wedge trench-slope basins: Example from the active Hikurangi margin, New Zealand. Barbara Claussmann, Julien Bailleul, Frank Chanier, Geoffroy Mahieux, Adam McArthur, Sergio Courtade, Per Salomonsen, Bruno Vendeville, Daniel Tetzlaff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
P0209. Improved anisotropic singularity index mapping method in support of mineral exploration Wenlei Wang, Qiuming Cheng, Shengyuan Zhang, Jie Zhao . . . . . . . . P0210. Modeling and simulation of geochemical reactions dring acid preflush to improve conformance control of pH-sensitive polymer flooding Hossein Younesian-Farid, Saeid Sadeghnejad . . . . . . . . . . . . . . . P0211. Numerical Simulation of Metallogenic Process in Zhuxi Giant Tungsten Deposit Qinglin Xia, Tongfei Li, Guanghui Chen . . . . . . . . . . . . . . . . . . P0212. On the size-distribution of karst depressions: lognormal or power models? Eulogio Pardo-Igúzquiza, Telbisz Tamás, Peter Dowd . . . . . . . . . . . P0213. 3D pathway modeling and hydrocarbon migration and accumulation modeling Qiulin Guo, Man Zheng, Jingdu Yu, Wei Yan, Shiyun Mi . . . . . . . . . P0214. A spatial causality method to identify the landslide-induced natural hazard cascades Anne-Laure Argentin, Günther Prasicek, Jörg Robl, Daniel Hölbling, Barbara Friedl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
P0215. Copper ore quality tracking in belt conveyor system using simulation tools Piotr Bardzinski, Leszek Jurdziak, Witold Kawalec, Robert Król . . . . . . P0216. Stochastic upscaling of hydrodynamic dispersion and retardation factor based on laboratory experiments Vanessa Godoy, Lázaro Zuquette, J. Jaime Gómez-Hernández . . . . . . . P0218. The relationship between the pacific subduction and fractal dimension of granitoids in Late Mesozoic, Great Xing’an Range, Northeast China Pingping Zhu, Qiuming Cheng . . . . . . . . . . . . . . . . . . . . . . . P0219. Using visible near-infrared spectroscopy and imaging to estimate heavy metals in black soils of northeast China Lu Wang, Maozhi Wang, Bingli Liu . . . . . . . . . . . . . . . . . . . . P0220. DISIRE Experiments of Ore Tracking in the KGHM Underground Copper Ore Mine Piotr Bardzinski, Leszek Jurdziak, Witold Kawalec, Robert Król . . . . . . P0221. BIM-Based Method for Site Investigation in Geotechnical Projects Junqiang Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T01. Compositional Data Analysis for Geochemical Data T0101. Replacement of values above an upper detection limit in compositions Dominika Miksova, Peter Filzmoser, Maarit Middleton . . . . . . . . . . 10
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IAMG2018 - Short Abstracts T0102. Estimation of regionalised compositions with recovery of original units Vera Pawlowsky-Glahn, J.A. Martín-Fernández, Juan José Egozcue, Ricardo A. Olea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
98 T0103. Incorporating analytical errors in log-ratio based compositional discriminant analysis Solveig Pospiech, Raimon Tolosana-Delgado . . . . . . . . . . . . . . . . 98 T0104. Geochemistry of surface waters of the Tiber River basin: Compositional Data Analysis approach Caterina Gozzi, Peter Filzmoser, Orlando Vaselli, Antonella Buccianti . . 99 T0105. Mapping gold pathfinder metal ratios in Northern Nevada, USA A compositional analysis approach Luis Braga, Jean B.B. Reis . . . . . . . . . . . . . . . . . . . . . . . . 99 T0106. Model Construction of Three-dimensional Multi-Fractal Singularity Analysis and Application in Deep Mineral Exploration BingLi Liu, Ke Guo . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 T0107. New insights on the self-organizing maps for compositional data: analysis of coal combustion products with an application to a Wyoming power plant Josep A. Martín-Fernández, Mark A. Engle, Leslie F. Ruppert, Ricardo A. Olea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
T0108. A CoDa approach to element chemostratigraphy of the nian/Carboniferous boundary Karel Hron, Kamila Fačevicová, Ondřej Bábek, Tomáš Kumpan . . T0109. Multielement geochemical modelling for pollution in the plains – quantifying the spatial relationship Jan Skála . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Devo. . . . 101 flood. . . . 101
T02. Machine Learning for Geoscience Modelling T0201. Clustering of environmental data using local fractality concept and machine learning Mikhail Kanevski, Mohamed Laib, Fabian Guignard . . . . . . . . . . . . T0202. Domaining with Decision Trees and Geostatistical Simulation Gunes Ertunc, A. Erhan Tercan . . . . . . . . . . . . . . . . . . . . . . T0203. Integration of geologically interpretative features into machine learning facies classification Julie Halotel, Vasily Demyanov . . . . . . . . . . . . . . . . . . . . . . T0204. Automated lithofacies classification of the Jurassic sequence using machine learning on a large structured well database Harald W. Bøe, Kristian B. Brandsegg, Kenneth Duffaut, Alenka Crne . . T0205. Neural network classification to improve geological and engineering understating for more reliable reservoir prediction
103
103 104
104
105
Elena Kharyba, Vasily Demyanov, Andrey Antropov, Luka Malencic, Leonid Stulov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
T0206. Methodology of fast well log interpretation based on deep Learning models Alexander A. Reshytko, Maria Golitsyna, Dmitry Egorov, Nikita Bukhanov, Artyom Semenikhin, Oksana Osmonalieva, Boris Belozerov . . . . . . . . 106
T0207. Stochastic Simulation with Generative Adversarial Networks Lukas Mosser, Olivier Dubrule, Martin J. Blunt . . . . . . . . . . . . . . 106 T0208. Probabilistic inversion using forward models based on Machine Learning Thomas Mejer Hansen, Knud Skou Cordua, Tue-Holm Jensen . . . . . . 107
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Contents T0209. Influence of input data quantity on accuracy of reservoir properties prediction with machine learning algorithms Dmitry V. Egorov, Nikita V. Bukhanov, Boris Belozerov . . . . . . . . . 107 T0210. Efficient uncertainty quantification of reservoir productions by stacked autoencoder-based clustering Kyungbook Lee, Taehun Lee, Jaejun Kim, Byeongcheol Kang, Changhyup Park, Hyundon Shin, Jonggeun Choe . . . . . . . . . . . . . . . . . . . 108
T0211. GemPy: Towards high dimensionality problems in structural geological modeling as Bayesian inference Miguel de la Varga, Florian Wellmann . . . . . . . . . . . . . . . . . . . 109 T03. Predictive Modelling of Resources and Hazards: Reliability and Uncertainty 111 T0301. Mineral occurrence target mapping: a general iterative strategy in prediction modelling for mineral exploration Andrea G. Fabbri, Chang-Jo Chung . . . . . . . . . . . . . . . . . . . . 111 T0302. Prospectivity mapping for porphyry copper-molybdenum mineralization in the Gobi desert covered area, Eastern Tianshan, China Fan Xiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 T0303. Integration of 3D geostatistical models of lithology, physical properties and element concentrations for metal deposit imaging in a seafloor hydrothermal vent area Vitor Ribeiro de Sá . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 T0304. Multifractal modeling of worldwide metal size-frequency distributions Frits Agterberg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 T0305. Quantifying Uncertainty on 3D Geological Surfaces Using Level Sets with Stochastic Motion Liang Yang, Jef Caers . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 T0306. Radon priority areas as random objects Peter Bossew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 T0307. Could immersive visualization improve geological hazard perception? Hans-Balder Havenith . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 T0308. The fractality of landslides’ spatial association with spatial factors Emmanuel John Carranza, Renguang Zuo . . . . . . . . . . . . . . . . . 115 T0309. The model of cyclic exogenous processes and natural risk assessment Alexey Victorov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 T0310. Probabilistic modeling for transport and communication network purpose in the taiga zone Olga Trapeznikova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 T04. Geomathematics and Marine Geosciences T0401. Mathematical Marine Geosciences: A Time Series Paleo Perspective Manfred Mudelsee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T0402. Singularity analysis of extreme events occurred along oceanic plate boundaries Qiuming Cheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T0403. „Noise“ – an integral part of climate modelling Hans von Storch, Xueen Chen, Shengquan Tang . . . . . . . . . . . . . .
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119
119
120 120
IAMG2018 - Short Abstracts T0404. Enhanced Principal Tensor Analysis (PTA SSA): a tool for multiway geological data reconstructions Sergey Kotov, Heiko Paelike . . . . . . . . . . . . . . . . . . . . . . . . 121 T0405. Multivariate geostatistical analysis of sedimentological and geochemical facies of a polymetallic nodule accumulation area within the Clarion-Clipperton Facture Zone, equatorial northern Pacific Ocean Łukasz Maciąg, Jan Harff . . . . . . . . . . . . . . . . . . . . . . . . . 121 T0406. Mathematics for CO2 geological storage Di Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 T0407. Models to display coastline change as paleo- and future scenarios Jan Harff, Andreas Groh, Joanna Dudzinska-Nowak, Andrzej Osadczuk, Ryszard K. Borowka, Hongjun Chen, Jakub Miluch, Peter Feldens, Ping Xiong, Yugen Ni, Wenyan Zhang . . . . . . . . . . . . . . . . . . . . . 122
T05. Dimensionality Reduction and Local Methods for Big Spatial and Space-time Data 125 T0501. Non-stationary environmental and meteorological data interpolation using principal component regression Konstantin Krivoruchko, Kevin A. Butler . . . . . . . . . . . . . . . . . 125 T0502. Unsupervised landform classification using automatic scale selection and Gaussian mixture model capable of distinguishing between different types of lowlands Jaroslaw Jasiewicz, Tomasz Stepinski . . . . . . . . . . . . . . . . . . . 126 T0503. Effective Probability Distributions for Spatially Dependent Processes Anastassia Baxevani, Dionissios Hristopulos . . . . . . . . . . . . . . . . 126 T0504. Introduction to a Stochastic Local Interaction Model and Applications Dionissios Hristopulos, Andreas Pavlidis, Vasiliki Agou, Giota Gkafa . . . 127 T06. Developments in Methods and Software Tools for Assessment 129 of Non-Renewable Resources T0601. Big data-based mapping mineral prospectivity Renguang Zuo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 T0602. Using space-time cubes for visualization, exploratory data analysis, in-depth data analysis, and to inform policy Joshua Coyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 T0603. Multi-Point Statistics for Tailings Deposits Sangga Rima Roman Selia, Raimon Tolosana-Delgado, K. Gerald van den Boogaart, Helmut Schaeben . . . . . . . . . . . . . . . . . . . . . . . . 130
T0604. Spatial analysis of mineral deposit distribution: examples in the Carajás Mineral Province, Brazilian Amazon Carlos Roberto de Souza Filho, Paulo Haddad-Martim, Emmanuel John Carranza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
T0605. An empirical approach for defining continuous fuzzy memberships for prospectivity modelling of Central Lapland Greenstone Belt Johanna Torppa, Vesa Nykänen, Ferenc Molnár . . . . . . . . . . . . . . 131 T0606. A 3D subsurface model of the Erzgebirge for 3D mineral potential mapping of Sn-W deposits with artificial neural networks Andreas Brosig, Andreas Knobloch, Claus Legler, Peggy Hielscher, Sven Heico Etzold, Enrico Kallmeier, Peter Bock, Andreas Barth . . . . . . . . 132
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Contents T0607. An emergent self-organizing map and compositional data analysis approach to predicting rare earth element potential in hydrocarbon produced waters of the United States Mark A. Engle, Charles W. Nye, Ghanashyam Neupane, Scott A. Quillinan, J. Fred McLaughlin, Travis McLing, Josep A. Martín-Fernández . . . . . 133
T0610. Mineral potential mapping and resource estimation with artificial neural networks using the advangeo® Prediction Software: Background, case studies and experiences Andreas Knobloch, Andreas Barth, Andreas Brosig, Thomas Kuhn, Daniel Boamah, Kwame Boamah, Henrik Kaufmann . . . . . . . . . . . . . . . 133
T0611. New ArcSDM5 toolbox used for orogenic gold prospectivity modeling within Northern Fennoscandian Shield, Finland Vesa Nykänen, Maarit Middleton, Tero Niiranen, Tero Rönkkö, Janne Kallunki, Juha Strengell, Kimmo Korhonen . . . . . . . . . . . . . . . . 134
T0612. U.S. Geological Survey Advancements in Mineral Resource Assessment Methods, Workflows, and Software Tools Mark J. Mihalasky . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 T0613. Integrating Mineral Prospectivity Modelling into the Three-Part Assessment Method: The MAP Software Kalevi Rasilainen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 T0614. A free software for pore-scale modelling: finite-difference method Stokes solver (FDMSS) for 3D pore geometries Kirill Gerke, Roman V. Valisyev, Siarhei Khirevich, Daniel Collins, Marina V. Karsanina, Timofey O. Sizonenko, Dmitry V. Korost, Sébastien Lamontagne, Dirk Mallants . . . . . . . . . . . . . . . . . . . . . . . . 136
T0615. Development and application of petroleum resources assessment system based on network environment and database Mi Shiyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 T07. Applied Geoinformatics for Mineral Exploration 139 T0701. 3D structure modeling and fractal analysis for exploration targeting and mineral resources assessment in Luanchuan polymetallic district Gongwen Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 T0702. Objectively grading geophysical interpretations and modelling mineral system uncertainties to generate robust exploration targets Joel N Burkin, Mark D Lindsay, Sandra A Occhipinti, Eun-Jung Holden, David Nathan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
T0703. Hierarchical approach to regional geological modelling Andrei Sidorov jn, Andrei Sidorov, Andrei Plavnik . . . . . . . . . . . . . T0704. Spatial distribution characteristics and mineral prospectivity mapping for tungsten polymetallic deposits in the Nanling region, China Tongfei Li, Qinglin Xia, Mengyang Zhao, Shuai Leng . . . . . . . . . . . T0705. Multifractal modeling in wavelet domain for identifying anomalies caused by deep mineral resources Guoxiong Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T0706. Application of fractal models to delineate mineralized zones in the Pulang porphyry copper deposit, Yunnan, Southwest China Xiaochen Wang, Qinglin Xia . . . . . . . . . . . . . . . . . . . . . . . . T0707. Chemical responses to hydraulic fracturing at magmatic hydrothermal transition: insight from numerical modeling Xiangchong Liu, Dehui Zhang . . . . . . . . . . . . . . . . . . . . . . .
14
141
141
142
142
143
IAMG2018 - Short Abstracts T0708. Characteristics and resource potentials of the source rocks in Cambrian System, eastern Sichuan Basin, China Man Zheng, Man Zheng, Qiulin Guo, Jingdu Yu, Jianhua Wang . . . . . 143 T0709. The predictor modelling for the magmatic type mineral resources based on the comprehensive information: a case in the Dazaohuo area, East Kunlun of northwest China ning cui, Keyan Xiao, Jiannan Liu, Jianping Chen, Zhuosheng Liu . . . . 144 T08. Tools for Analysis of Non-quantitative and Miscellaneous Data. Its Applications 145 T0801. A Series of Software Systems for Verifying Mining Rights in China Yongzhi Wang, Yongjie Tan . . . . . . . . . . . . . . . . . . . . . . . . 145 T0802. Parametric Assessment of the Quality of Estimation Marek Ogryzek, Ryszard Źróbek, Mateusz Ciski . . . . . . . . . . . . . . 146 T09. Stratigraphic Forward Modeling T0901. A Digital Flume Tank Cedric Griffiths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T0902. Assessing the potential of response surfaces to perform risk analysis and data assimilation in stratigraphic forward modeling Veronique Gervais, Didier Granjeon, Patrick Rasolofosaon . . . . . . . . T0903. Automated Inverse Stratigraphic Modeling Using Differential Evolution Yanfeng Liu, Taizhong Duan, Wenbiao Zhang, Mingchuan Wang . . . . . T0904. Evaluating the structural control over carbonate platforms developed in syn-rift settings
147 147
148
148
Isabella Masiero, Peter Burgess, Lucy Manifold, Cathy Hollis, Johanne Nergaard Grinde, Rob Gawthorpe, Atle Rotevatn, Isabelle Lecomte . . . . 149
T0905. How to quantify the initial source-rock properties at a basin-scale from a stratigraphic numerical forward model ? Benoit Chauveau, Didier Granjeon, Alina-Berenice Christ . . . . . . . . . 150 T0906. Sediment load and transport estimation using random walk theory Daniel Tetzlaff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 T0907. The weighted curvature minimization: a correction to thickness variations in implicit structural modeling Julien Renaudeau, Frantz Maerten, Emmanuel Malvesin, Guillaume Caumon 151 Contributions by topic
153
Author Index
158
15
Contents
16
G00 Plenary Talks
G0001. Mathematics overcame the statistical problems of closure: Has it solved real-world geochemical problems? Mark A. Engle, Nicholas E. Pingitore Mathematical techniques, such as Compositional Data Analysis (CoDA), have resolved statistical problems inherent with analysis of “closed” array data (e.g., constant sum, volume, mass, etc.), a common form of data in geochemistry. However, some of us still struggle to interpret the “opened” results. To provide insight into the apparent confusion, we consider simple examples of data closed to a sample basis (e.g., percent, per mass, per volume). We define two systems that generate closed data arrays (physically open versus closed) and two types of components [immutable (e.g., elements, sand grains) and reactive (e.g., minerals or molecules]. From these definitions, we consider 4 scenarios available for study: 1. Closed system of immutable components: No covariance, no process to study. 2. Closed system of reactive components: Covariance of a closed data in a physically closed system of reactive parts can only be affected by reactions between the parts. Such a system suffers the mathematical effects of closure but, arguably, not the conceptual effects. 3. Open system of immutable parts: The only process affecting a closed data array consisting of immutable parts is addition or subtraction of parts from the sample basis. If the bounds of the closed array are removed, no covariance can exist between its parts. 4. Open system of reactive parts: Covariance of a closed data array in a physically open system made up of reactive parts can be affected by both the addition/subtraction of parts within the system basis and by reactions between parts. We discuss the statistical questions geochemists ask in each of these 4 scenarios and evaluate how effectively mathematical approaches such as CoDA provide appropriate answers. Our findings suggest that there is still work required to understand the conceptual implications of mathematical solutions to closed array data of geochemical problems.
G0002. Multiple-point geostatistics: when do they work, when do they not work Gregoire Mariethoz Multiple-point geostatistics have significantly developed in the last years, becoming an integral part of the spatial modeling toolbox for a range of applications. 17
G00. Plenary Talks In some cases, these methods can offer possibilities that are not available within the framework of covariance-based geostatistics, such as when connectivity constraints are important or when large amounts of data are available to inform high-order properties. In other cases however, multiple-point geostatistics is not applicable, either because of a lack of training data, because of computational constraints, or when spatial dependence needs to be formulated in an analytical manner. This talk will survey some practical examples of applications that work and that do not work with multiple-point geostatistics, based on different fields of study including Earth observation, subsurface modeling, time series analysis or atmospheric science. It will provide some guidelines and tools on how to choose a proper a training image, or how to design one then it is not readily available. Examples will also be shown where multiple-point statistics cannot be used efficiently, and which alternative covariance-based approaches can then be implemented.
G0003. Spatial decorrelation methods revisited Ute Mueller When dealing with multivariate geostatistical data it is common to apply a decorrelation method as part of the workflow. The most common one is principal component analysis (PCA) based on a spectral decomposition or a singular value decomposition of the covariance matrix of the data. The application of a PCA results in factors that are decorrelated in a classical statistical sense, but spatial cross correlation might still persist, once the spatial behaviour of the data is considered. A more general decorrelation method is the method of minimum/maximum autocorrelation factors (MAF), where a pair of matrices, typically the covariance matrix and a covariance matrix at a separation distance h, are used to transform the data to factors which are uncorrelated at lag 0 and at lag h. In essence MAF is nothing other but a PCA followed by a further orthogonal transformation of the PCs. When dealing with process discovery in the analysis of geochemical data, classification based on MAF results in better classification outcomes than PCA, as the spatial nature of the data is accounted for explicitly, although to a limited extent. In the case of geostatistical simulation, it is again the case that in general simulation based on MAF factors produces superior results to those based on PCA. Considering the covariance function of the data, the effect of MAF is to diagonalise two of the covariance matrices exactly, while the remaining ones are approximately diagonal. MAF is thus a non-orthogonal approximate diagonaliser of a set of symmetric matrices. In blind source separation, more general approaches for approximate joint diagonalisation (AJD) have been developed. In contrast to PCA or MAF, these approximate diagonalisers are iterative and based on different objective functions. We discuss different AJD methods and compare their performance to MAF or PCA
G0004. The High-order Stochastic Sequential Simulation Framework: A review with examples Roussos Dimitrakopoulos The high-order sequential simulation framework for modelling complex nonGaussian, spatially distributed and variant attributes of geological phenomena has been introduced over the last few years as an alternative to the well-established multi-point statistics based approaches. A major aspect of the new framework is the focus on data-driven, rather that analogue-driven, simulations, supported by consistency between low- and high-order spatial relations through the use of spatial 18
IAMG2018 - Short Abstracts
G0006
cumulants. This presentation reviews the related developments along with their pros and cons. First, the initial efforts are presented where conditional probability density functions in the sequential simulation process are approximated using Legendre polynomials. This is followed by the introduction and use of Legendre-like orthogonal splines and extensions dealing with the joint simulation of spatially correlated variables through decorrelation, the direct simulation of blocks from point data, and the simulation of categorical variables. All approaches presented are based on the use of high-order spatial statistics inferred from the available data, complemented by geological analogues (training images). Examples and specific practical aspects, along with contributions and limits, are demonstrated using both complete datasets as well as actual applications in simulating different gold, copper, and iron ore deposits; the later applications show the effects of the new framework on metal production planning and forecasting. The presentation concludes with new directions in this area of research engaging concepts from statistical learning.
G0005. Some aspects of geostatistical simulations Christian Lantuéjoul Resorting to simulations has a long history in the geostastistical community. It started with the introduction of the turning bands operator that allows to generate realizations of multidimensional random fields using those of unidimensional stochastic processes (Matheron, 1972). Then followed the design of an algorithm for simulating Gaussian random fields conditionally on data (Journel and Matheron, 1975). Nowadays, simulations are ubiquitous. Algorithms exist for a wide range of spatial stochastic models on both Euclidean and non Euclidean spaces. Morereover, simulations can be used as tools for testing hypotheses. Three novel examples are presented to illustrate those points. The first example deals with the conditional simulation of max-stable random fields. Designed for handling extremes, these models typically have infinite moments, which precludes their prediction using kriging techniques. Starting from the reference work by Dombry and Eyi-Minko (2013), latent variables are introduced to make this algorithm more robust from a numerical standpoint, which makes it possible to accommodate more conditioning data. The second example concerns the simulation of Gaussian random fields on a sphere. The current algorithm is based on a truncated Kahrhunen-Loeve expansion. As a consequence, the simulations produced are too smooth. To cope with this problem, a spectral algorithm is introduced instead. The third example bears on continuum percolation. Experiments suggest that large simulation domains are not required to estimate the percolation threshold of Boolean models of isotropic objects, such as random disks or Poisson polygons. Keywords: Max-stable random fields, spectral method, continuum percolation, Boolean model.
G0006. Geological objects and physical parameter fields in the subsurface: a review Guillaume Caumon Geologists and geophysicists often approach the study of the Earth using different and complementary perspectives. To simplify, geologists like to define and study objects and make hypotheses about their origin, whereas geophysicists often see the earth as a large, mostly unknown multivariate parameter field controlling complex physical processes. In this talk, I discuss some strategies to combine both 19
G00. Plenary Talks approaches. In particular, I review some practical and theoretical frameworks associating petrophysical heterogeneities to the geometry and the history of geological objects. These frameworks open interesting perspectives to define prior parameter space in geophysical inverse problems, which can be consequential in underconstrained cases.
20
G01 3D/4D Geomodeling Helmut Schaeben
G0101. 3D geological model meshing method based on corner-point grid and First-sedimentary-Last-structure approach Xuechao Wu, Gang Liu, Qiyu Chen, Yang Li In the process of 3D model meshing of oil and gas structure by using cornerpoint grid, the faults are often forcibly bounded by the adjacent grid edges. The corresponding grids need to be parallel to the faults and cannot be cut by the faults. This leads to the distortion of the three-dimensional geological grid produced in the modeling process, and the result of the sedimentary face simulation is distorted. Therefore, a 3D geological model meshing method based on corner-point grid and First-sedimentary-Last-structure approach is proposed. Firstly, it does not consider the existence of faults. It adopts UVT conversion technology to restore the strata to the paleo sedimentary state in the time domain and use the corner-point grid data model to carry out the body element dissection. Then the corner-point grid model is cut by the fault surface. Finally, the model is converted from the time domain to the depth domain through the XYZ coordinate transformation. Thus, a continuous 3D geological model without grid distortion is obtained. This method has achieved good application results in Shang 401 block with complex Y type faults in Shengli Oilfield. It has made the geological grid suitable to the complex strata and preserved volume and distance information of the simulated target body.
G0102. 3D modelling and visualization of the resources warehouse of mineral rock specimens Chen Zhijun Identification of mineral rock specimens are essential skills for the geological researchers and students. however, they are often not accessible easily. With the increasing development of the technology of the multi-view image 3D reconstruction and VR/AR, The 3D modelling and visualization for the huge amount of the mineral rock specimens become very easy, economical and practical. The abundant 3D specimens of minerals, rocks and ores in China University of Geosciences (Wuhan) has been successfully build using the multi-view image 3D reconstruction technology in recently years. And the VR/AR resources warehouse are published on website. It helps the researcher and students save the rock images in nature forever, capture the 3D textures of anytime, anywhere and improve the study of the level of the digital rock.
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G01. 3D/4D Geomodeling
G0103. A graph-based approach to simplify subsurface structural models and assess the impact on physical modeling Pierre Anquez, Guillaume Caumon, Jeanne Pellerin Surface-based geological models are inherently composed of complex geometries caused by various geological features such as thin layers, tangential stratigraphic unconformities, or small fault displacements. These small features constrain the mesh edge lengths as well as the angles, therefore strongly constraining mesh cell quality. To have a good mesh quality and ensure the accuracy of geomechanical or flow simulations, one solution is to use a very fine mesh and highly increase the number mesh elements but this increases computation costs. A second solution is to modify the geological model by displacing or merging locally its boundaries with to simplify the model before generating the mesh. We introduce a formal approach to detect and simplify geological model complex features. These features are modeled by a graph that provides a theoretical framework to operate and correct the input model. There are generally several options to simplify a given structural model: e.g. by modifying or not the connectivity between geological components. These possible operations to simplify the geometrical complexities are characterized by graph elementary operations. In our approach, we first operate on the graph aiming at removing all the edges representing invalid features. The second step is to account for these topological and/or geometrical changes in the initial model. We compare the results of physical simulations between initial 2D models (i.e. cross-sections) and three edited models: (1) models with refined meshes without simplification, (2) models with only geometrical simplifications (without affecting the model connectivity), and (3) models with connectivity modifications. This allows us to study the importance of preserving or not the model connectivity throughout simplification for various physical phenomena.
G0104. Automatic strata comparison method of urban geological section for 3D modelling based on knowledge graph Gang Liu, Qiyu Chen, Fonan Zhong, Jiyin Zhang, Xuechao Wu As a kind of fundamental and effective method, section-based 3D modelling method remain important challenge of validity and efficiency. The validity means the model should conform to the law of geological evolution, which is the key constraint, as well as the efficiency is requested to realize modelling automatically. We propose a new method based on knowledge graph to organize and express the geological knowledge for section strata comparison, and automatic generation of geological sections based on drill hole data is realized by means of stratigraphic comparison and topological reconstruction algorithm. For application research of urban stratigraphic comparison, the stratigraphic comparison rules of Quaternary strata are summarized. Based on the basic law of stratigraphy, spatial distribution type and method of stratigraphic classification, the expression form of stratigraphic comparison rules and detailed content based on knowledge graph is determined. The correlation algorithm is set up by knowledge logic, such as minimum error rule, equal opportunity rule, loose-flat rule, thickness balance rule, Walther facies law and pinch-out rule. The stratigraphic data model is constructed according to the classification of stratigraphic distribution and its application. Using object22
IAMG2018 - Short Abstracts
G0106
oriented structure model and knowledge acquisition technology, the knowledge base and method library are established. Based on the knowledge driven mechanism, the automatic drawing of geological section and the construction of city 3D geological model are realized by combining the automatic comparison algorithm and the topological reconstruction algorithm. The case study of 3D urban geological survey of Fuzhou, China, are performed with satisfactory results.
G0105. Geophysical forward modelling with GECCO tools for heterogeneous lithological associations – the closed Mullikkoräme massive volcanic zinc sulphide mine in Finland as a case study Eevaliisa Laine, Johan Ersfolk, Ilkka Suppala, Marit Wennerström, Jan Westerholm A central task in mine site evaluations and 3D mineral potential studies is to collect data on multiple spatial scales and then use inverse methods to infer the location and extent of economically interesting mineral deposits. Data sets comprise, for example, airborne and ground geophysical data, drill hole data, geological maps and cross sections, drill core logs, and geochemical data. Directly observed geological information is often sparse (e.g. drill holes) and subsurface geology has to be inferred through interpretation and inversion of measured geophysical data. Project GECCO combines expertise in high performance computing and geomodelling, and aims to develop tools for faster geological modelling in a powerful computing environment. The present study deals with the problems appearing in the geophysical modelling of heterogeneous lithological associations. Many ore mineralizations are associated with such heterogeneous lithological associations caused by complicated tectonic history, metamorphism and later alteration. It is difficult to assign any constant petrophysical properties for different lithologies. The present study uses drill core data from the closed Mullikkoräme massive volcanic zinc sulphide mine in Finland. The Mullikkoräme formation is a north-south trending bimodal volcanic formation composed of basaltic pillow metalavas (west) and rhyolitic felsic metavolcanic rocks (east). The mineralization is polymetallic consisting of small Zn and Pb rich sulphide lenses. To the southeast, the mineralization is bordered by a wide shear zone. Geological and petrophysical properties are simulated by dense 10x10x10 - 100x100x100 voxets with a voxel size of 1 m3. The simulation is constrained by geological and petrophysical modelling. Simulated voxets are used to study lateral and vertical changes of upscaled petrophysical properties and the overall geophysical responses are compared with measured geophysical fields. Graphical processing units GPUs are used in the simulations and the forward modelling with GECCO tools.
G0106. Geostatistical Facies Modelling of Cyclicity, Rhythmicity and Diagenesis in Sedimentary Sequences Thomas Le Blevec, Olivier Dubrule, Cédric M. John, Gary J. Hampson A geostatistical facies modelling method is presented, which incorporates fundamental sedimentological principles and thus improves the realism of earth models.
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G01. 3D/4D Geomodeling More precisely, the method reproduces upward-coarsening or upward-fining cycles at the reservoir scale, and generates models with asymmetrical behaviour in different directions. Rhythmicity, Walther’s law, and lateral cyclicity can also be modelled, together with the possibility to superimpose several correlated facies fields, which is useful for modelling diagenesis. The method is based upon Pluri-Gaussian Simulations and uses a shifted linear model of co-regionalization. This allows creation of an asymmetrical facies ordering along the direction of the shift, resulting in cyclicity. The cyclicity is quantified by transiograms, an extension of the indicator cross-variogram which can quantify asymmetry. Although Markov Chain models can reproduce cyclicity, they are limited to exponential covariances, and are difficult to extend to two and three dimensions. We use separable covariance models, which allows use of dampened hole-effect or classical monotonic covariances in any direction of the three dimensional space. Thus, rhythmicity can be modelled only in specific directions if required. The screening property of separable covariance models allows very fast and efficient conditioning to the data with Gibbs sampling and simple post-kriging. Non stationarity is easily incorporated in the method by varying the thresholds of the truncation rule, which can affect cyclicity. Diagenesis is modelled by the use of a bi-PGS approach together with shifts and correlations between the different facies fields. The use of three-dimensional truncation diagrams represents the relationship between diagenesis and depositional facies. The method is finally tested on a reservoir-scale case study, the Triassic Latemar carbonate platform, which exhibits cyclicity, rhythmicity and syndepositional diagenesis.
G0107. Multi-Physics Joint Inversion - a Flexible Expert-distributed Approach Peter L. Smilde, Christina Mueller, Markus H. Krieger, Soegun Petersen The properties of a multi-physics joint inversion application are discussed, that takes into account the information from several geophysical methods, from well and laboratory data and from geological concepts to return a much more reliable interpretation of the model space. It facilitates to merge all these sources of information and the knowledge and capabilities of usually several expert groups in a well-balanced way. A practical approach is described, that helps to solve usual problems for such set-ups, like the optimal selection of common or compatible parametrizations, the flexible definition of well-delimiting, but non-conflicting boundary conditions, and satisfactory relative weighting of the diverse contributions. Furthermore, it is possible to incorporate forward modelling computations at each of the partners’ sites into the joint inversion framework. To this purpose a most suitable integration method of several suggested ones can be implemented, optimally taking into account the special knowledge, benefits and limits of each of contributing calculation methods and working groups. A practical realization based on these concepts has already proven its feasibility and is available to be extended with additional methods.
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G0108. Plurigaussian Simulation of Geological Domains in the Presence of Spatial Trends Nasser Madani, Xavier Emery Plurigaussian simulation is used in subsurface modelling to quantify the uncertainty in the boundaries of geological domains. The conventional model relies on strong stationarity assumptions and is restricted in the number of domains and their spatial behaviour over the region of interest. This paper proposes model improvements to account for the cases when the number of geological domains is large and when spatial trends or zonations arise from geological formations. The proposed plurigaussian approach consists in truncating intrinsic random fields of order k (IRF-k), instead of stationary fields, and is illustrated through the probabilistic modelling of seven rock domains in a copper deposit located in the Chilean Central Andes. Despite the scarcity of conditioning drill hole data, the non-stationary plurigaussian model shows a remarkable agreement between the simulated domains and the rock type domains interpreted by mining geologists, while the conventional stationary plurigaussian model fails at reproducing the expected zonation.
G0109. Resources Modelling of Alluvial Tin Deposit in Former Artisanal Mines Area using Geophysical and Geostatistical Methods Mohamad Nur Heriawan, Guntan Viliarso Seran, Olga Padmasari Anggraini, Wahyudi Widyatmoko Parnadi This research focuses on the problem faced by the tin mining industry in Indonesia where some parts of the concession is overlapped with the artisanal mines. Artisanal mines commonly disregard the environmental condition in post-mining, and moreover disrupt the ore reserve balance within the respected concession areas. For example, the artisanal mines activities which are located within the concession area of tin mining at Bangka Island, Indonesia, have been known for a long time to date. The company often face difficulty in estimating the ore reserve that still remain in the areas which formerly was the artisanal mines. This research considers the integration of geophysical and geostatistical methods to optimize the alluvial tin resource model in three dimensions (3D) in the former artisanal mines area. Geophysical method by using ground penetrating radar (GPR) with antennae frequency 100 MHz and validated using induced polarization (IP) is adequately effective to map the layer of alluvial deposit. The conditional geostatistics have been known to be sufficiently accurate to model the mineral resources from the limited number of drilling data. The layer of alluvial deposit interpreted by GPR method and layer position known by limited number of drilling data was jointly simulated using sequential gaussian co-simulation method. With the integration of these two-different data type, the modelling result of the remaining alluvial tin reserve become more optimum. So, this result can be used as an important information for the company to do mine valuation.
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G02 Compositional Data Analysis Vera Pawlowsky-Glahn, Juan José Egozcue
G0201. 3D soil texture mapping with L1-regularized multinomial logistic regression Milutin Pejović, Mladen Nikolić, Branislav Bajat Soil texture is one of the most important soil attributes. It is usually expressed as a sequence of percentages of different-sized soil particles which, at one sample, necessarily sum to 100%. Due to its significant influence on physical and chemical properties of soil, it is often necessary to have a model that can predict soil texture at specific location. In this study, we demonstrate the use of L1 regularized multinomial logistic regression for 3D mapping of soil texture (clay, silt, and sand) for whole territory of Netherlands. 3D mapping involves creating � model (which includes spatial covariates and soil depth as predictors) for spatial prediction at different soil depths. In general, L1 regularized logistic regression is widely used for binary classification problems, because it performs feature selection as part of efficient model training process. This study shows that L1 regularization is also very efficient in multinomial problems when the target is the distribution of closure components, like soil texture components. Non-regularized multinomial logistic regression is used as benchmark method. Models with interactions between spatial covariates and soil depth, as well as with polynomial expansion of depth, were also considered in the study, which enabled the comparison of regularized and non-regularized techniques when a large number of predictors is considered. Considering that L1 regularization requires tuning meta-parameters through cross-validation, for stringent evaluation, models were evaluated via nested cross-validation procedure. The results show that the regularization improves model accuracy at each component up to 20% in terms of R squared. As expected, the improvement is most noticeable for interaction models, in which case non-regularized models significantly overfit. Reason for this lies in sparse structure of regularized models which are less flexible than non-regularized models.
G0202. Block cokriging and the flow anamorphosis Raimon Tolosana-Delgado, K. Gerald van den Boogaart, Ute Mueller The flow anamorphosis is a multivariate transformation to Gaussianity that has the property to be affine equivariant, that is: to deliver the same result even if the data have been applied an arbitrary linear transformation. This makes it a very good way of transforming compositional data to multivariate normal scores, and 27
G02. Compositional Data Analysis therefore for the straightforward use of point-support multivariate geostatistics to regionalized compositional data. This contribution explores strategies to use the flow anamorphosis for block-support geostatistics, like block cokriging and cosimulation with a change of support. The key idea is to use Gaussian quadratures to approximate the spatial integral. The first step is to discretize the target block in a series of representative point supports, and to produce a point cokriging of the normal scores for them, together with their joint covariance matrix. The covariance is then applied a diagonalization, where the directions of very small variability are filtered out. For the remaining directions, multivariate Gauss-Hermite quadrature points and weights are computed. The points define a mesh of integration points around the vector of cokriged values. These are then back-transformed through the flow anamorphosis and the resulting compositional values are reblocked with the Gauss-Hermite weighting. The procedure requires a relatively high number of integration points to reach satisfactory accuracies, even more than Monte Carlo approaches to the block cokriging problem. However, these can be produced on the fly when required, and do not need to be jointly produced at the same time. Thus, one could produce cokriging estimates and covariance matrix first, and later on do the reblocking calculations block by block in parallel with the required level of accuracy. This decoupling cannot be reached with Monte Carlo approaches.
G0203. Decoupling processes from soil geochemistry: Mapping surficial/bedrock geochemical signatures in Northern Ireland Eric Grunsky, Jennifer McKinley, Ute Mueller The bedrock geology of Northern Ireland is represented by a stratigraphic record from the Mesoproterozoic to the Palaeogene. Glaciation and erosion has left more than 80% of the bedrock covered by superficial deposits, including glacial till postglacial alluvium and peat. These surficial deposits are derived from processes of weathering, groundwater effects, comminution, transport and sorting. The Tellus soil survey data is comprised of 6862 sites that were sampled at 20cm below surface on a non-aligned grid with each site representing 2 km2. Geochemical analyses were carried out using XRF technology. In addition, Au, Pd and Pt were analyzed using Fire Assay. Each element was assessed for values reported at less than the lower limit of detection (< LLD). Values reported at < LLD were replaced by imputed values in order to minimize any biases introduced by LLDs. A total of 50 elements, plus loss-on-ignition were used to characterize the geochemical signature of the soils. Logratio transforms were applied to deal with the compositional nature of the data. Each sample site was tagged with the corresponding Age Bracket based on a 1:500k regional geology map by the Geological Survey of Northern Ireland, plus a derived map of lithologies and a map of surficial materials. Various multivariate metrics (PCA, ICA, MAF) were used to discover/describe known and unknown processes in both the multi-element logratio- and the geospatial- domains. Training sets, derived from tagging Age Bracket, lithology and surficial deposits, along with empirical thresholding were used to decouple processes associated with lithologies and surficial processes. Classification of the soil chemistry and associated bedrock/surficial categories, was carried out using Random Forests. The results show that soil geochemistry can be used to distinguish between bedrock and surficial processes along with measures of uncertainty.
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G0204. Exploration targeting by multivariate compositional extrema K. Gerald van den Boogaart, Raimon Tolosana-Delgado, Jennifer McKinley Many different exploration targeting methods exist, like weights of evidence; inferring the probability of a deposit based on a local geology; genetic models identifying favourable conditions; and fractal based methods trying to identify regions of high value of certain fractal measures. This contribution proposes an approach potentially useful for deposits under cover: to find locations which are locally extrema on a certain spatial scale. While surface features typically dominate the absolute values of measurements, covered objects can still produce large halos of much smaller absolute value. Our method thus looks for halos at a certain spatial scale. It does so by estimating a band filtered negative of the second derivative of the random field from spatial data, either from regular data or from a geostatistical analysis. In a certain sense this is looking for local peakiness, but filters the high frequency noise from surface effects by means of signal processing methods. The local maxima and their surrounding are then taken as the potential targets. In a multivariate surface dataset, as provided by a geochemical exploration campaign, such a filter can be applied to the complete vector (i.e. the clr or ilr transformed compositions). This results is a nought mean compositional random field. Furthermore the bandwidth of the filter can be varied and considered as a third dimension. In this 3D map, we can again find various types of extremal points. The location on the 2D geographic space of the extreme value of the signal depends on the location of the deposit. Furthermore, the location on the third dimension relates to the deposit size or depth, and its compositional value describes its geochemical properties. We demonstrate the effects of the method with a regional geochemical exploration dataset.
G0205. How can we use compositional data for determining threshold values for environmental health assessment? Jennifer McKinley Baseline mapping and the calculation of threshold values are often used to assess environmental assessment for human health. A threshold is often set to differentiate between the level of concentration of an element that is deemed acceptable and that which is potentially toxic to human health. Potentially harmful elements may occur naturally in the soil or may result from diffuse anthropogenic sources. Regional geochemistry soil databases are increasingly being used to determine these baseline environmental assessments. The question is can we use a single elemental baseline or threshold to provide an accurate calculation of threshold values? Despite the limitation of relative abundances, individual raw geochemical maps are deemed fundamental to the use of geochemical maps for environmental assessments. This presentation examines what the compositional nature of geochemical data means for baseline mapping and the implications for health risk evaluation. A range of alternative compositionally compliant representations based on log-ratio and log-contrast approaches are explored to supplement the classical single component maps for environmental assessment. Case study examples are shown based on the Tellus soil geochemical dataset covering Northern Ireland, UK and renal disease data. 29
G02. Compositional Data Analysis
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G03 Data Assimilation and Data Integration Raimon Tolosana-Delgado
G0301. An Improved Method For Fracture Modeling Based On Principle Component Analysis And PR Model Shuang Sun, Jiagen Hou, Yuming Liu, Suihong Song Discrete Fracture Network (DFN) has become the research focus of fracture modeling because of its ability to better characterize the heterogeneity of fractured reservoirs. Due to limited hard data, the integration of multiple secondary data can reduce the uncertainty in prediction of fractured reservoirs and provide accurate fracture models. PR (Permanence of Ratio) model is a probability combination scheme under assuming that ratios of probability increments from different sources are constant. However, when many secondary data are considered and they are highly redundant, the resulting probability has a large deviation. An improved method based on principle component analysis (PCA) and PR model is proposed in this paper to integrate multiple secondary data for fracture modeling. Firstly, select several secondary data highly related to fracture development through correlation analyses, mainly including seismic and geology data. Secondly, PCA of these selected secondary data leads to fewer and independent principal components. Then, PR model is used to approximate the probability of fracture development for each cell through linking the individual probability that is computed using each principal component. Next, fracture density model is established by sequential Gaussian co-simulation using the interwell fracture probability body as constrains. Finally, under the constraints of the fracture occurrence statistical data and fracture density model, DFN is established by combination of the annealing simulation and the object-based marked point processes simulation. This improved fracture modeling technique is applied in a block of Honghe oilfield in Ordos Basin in the northwest of China, which is a tight reservoir dominated by braided river delta front sediments and develops fractures. DFN of this area is built by integration of five secondary data, including variance, structural dip, RMS amplitude, envelope and fault distance. The results are more reasonable and realistic to production data compared with fracture models with single secondary data.
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G0302. Data normalization approach in geological favorability about shale oil exploration for Shahejie Formation in the Liaohe Depression, Bohai Bay Basin, China Jingdu Yu, Man Zheng, Qiulin Guo Effective geological favorability based on data acquired in shale oil and gas exploration is essential, and it brings direct impacts on the accurate identification of “sweet spots” in an area. In this paper, a data normalization approach based on geological data and quantitative weights was introduced. Its application in the E2s32 shale formation in the Liaohe Depression, Bohai Bay Basin, China, indicates that the geological favorability in the study area is a dimensionless quantity sensitive to many parameters (e.g. faults and fractures). The proposed approach reflects less complexity and uncertainty caused by graphical superposition, and can realize clear and high-resolution qualification of geological favorability in all locations of the study area. According to the application of the approach in the E2s32 shale formation, the central and southwestern parts of the study area are believed to have great potential for exploration, which has been proved by drilling results. This approach is based on acquired data set but not specific to a certain case, so it is believed to be with a relatively broad applicability.
G0303. Data-driven fusion of multi-resolution digital elevation models and remote sensing imagery Luiz Gustavo Rasera, Gregoire Mariethoz, Stuart N. Lane Most of the Earth’s terrain is mapped at relatively coarse spatial resolution. However, even though high-resolution digital elevation models (DEMs) are essential tools for the study of fine-scale surface processes, such models cover only a small fraction of our planet. Nowadays, extensive databases of satellite images are widely available at high spatial and temporal resolutions. These images can be potentially used as auxiliary sources of information to improve the spatial and temporal resolution of DEMs. Here, we propose a data-driven fusion algorithm to downscale a target low-resolution DEM using multiple-point statistics simulation. The method relies on the integrative use of: 1) a high-resolution DEM from another betterinformed area (training data) as a prior sub-pixel spatial model; 2) and fine-scale multispectral satellite images as covariates to constrain the downscaling process. A pyramid-based simulation scheme is employed to reproduce multi-scale features present in the training data, and integrate multi-resolution terrain and imagery data. The simulation parameters are calibrated using an iterative optimization algorithm. Such framework requires the formulation of a multi-criteria loss function to determine an optimal parameter configuration for a given training data set and application domain. A case study is provided to illustrate the methodology.
G0304. Distributional Data Assimilation for Resource Model Updating Angel Prior Arce, Raimon Tolosana-Delgado, K. Gerald van den Boogaart, Joerg Benndorf, Alessandra Menafoglio Some geometallurgical properties are expressed as distributional variables that 32
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are characterized by probability density functions. In order to describe these geometallurgical properties in space considering the whole distribution and not only the a few moments. There are methods for statistical interpolation and simulation of distributional data in space. However, not methods are available to sequential incorporation of information back into the spatial model. The advantages of data assimilation algorithms for resource model updating in mining industry has been shown before for univariate and multivariate models. Based in the information collected during mining production about different features is becoming more popular due to sensor technology advances. The Ensemble Sequential Updating techniques provide a comprehensive solution for these challenges since, that allows to relate the potentially non-linear relation that exists between the resource and grade control model state variables and the observations. However, sensors might provide information about these observations in a distributional support. This study aims to develop and implement a data assimilation algorithm for distributional variables in mining settings. There are different challenges to face in order to understand how the information that proceed from sensors informs about the geostatistical models and how to feed updated information back to the resource model. The state variables of such models are actually distributions which are infinite dimensional supported. We use the Bayes spaces framework that allows the characterization of distributional data. Within this framework we develop a new mathematical tool for data assimilation process reproducing the complete information content embedded in distributional data. The distributional data assimilation approach is tested in a virtual assets models created as a fully controllable environment with particle size distribution properties. After validation, a sensitivity analysis investigates the effects of different parameters and derived practical implementation aspects for an effective application within an operating mine.
G0305. Importance of Initial Ensemble Design for Reliable History Matching – Comparison of Ensemble-based Methods Applied to the 3D Egg Model Byeongcheol Kang, Junyi Kim, Kyungbook Lee, Hoonyoung Jeong, Jonggeun Choe It is required to integrate various types of reservoir information to make reliable reservoir models. Ensemble Kalman filter (EnKF) and ensemble smoother (ES) have gained popularity in history matching because they are relatively easy to implement, parallelize, and couple with any reservoir simulator. EnKF takes an assimilation step at every observation time while ES does only a single global update at the last observation time. Thus, EnKF needs more computational cost than ES, but EnKF is commonly considered to have more stable convergence than ES. Both the ensemble-based methods have a computational cost issue because hundreds of reservoir models are necessary to represent high uncertainty in large scale reservoir systems. In this study, we propose a model selection scheme that selects and updates a set of models representing initial reservoir models prior to applying EnKF or ES. Our model selection scheme aims to save the computational cost by forward-simulating and updating only the representative models. After the dimension of initial reservoir models is reduced using principal component analysis, the models are grouped in the dimension-reduced space using the K-means clustering. Flow simulation is conducted in only the representative models
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G03. Data Assimilation and Data Integration of the groups, and then the representative model that honors observed data most is selected. Only the group members of the selected representative model are updated using EnKF and ES. We demonstrate in a set of 3D egg models that EnKF and ES provide more accurate updated reservoir models quickly using our model selection scheme. The results show importance of the good initial models and we compare the history matching results, using different ensemble sizes and optimization methods.
G0306. Localization of Sulfide Mineralization using 2D Electrical Resistivity Tomography and Enhanced Local Wave number techniques over Bouguer Gravity anomaly Ashok Kumar Gupta, Roshan k Singh, Shalivahan Srivastava, Shovana Mondal Electrical Resistivity Tomography (ERT) in conjunction with gravity surveys were carried out over Paleo-Proterozoic metavolcanics, Dhanjori basin, Eastern Singhbhum to delineate sulfide mineralization. The sulfide mineralization is associated with the metavolcanics and meta-tuff sequences of the Dhanjori Groups. The occurrences of chalcopyrite, pyrite, pyrrhotite and cuprite ore mineral assemblage have been reported both in massive to braided veins and disseminated form in the study area. ERT data using the Dipole-dipole array and gravity data have been acquired along the same line at a station spacing of 10 m. Gauss-Newton optimization has been used for inversion of ERT data. It shows the presence of a 40 m thick conductor lying between 210 m to 310 m at a depth of 40 m. This conductor is embedded in the resistive medium. The anomalous low resistivity coincides with the high Bouguer gravity anomaly. The Enhanced Local Wavenumber (ELW) technique has been used to interpret Bouguer gravity anomaly. ELW provides information about the horizontal location, depth to the top and shape of the causative source. The inferences from the ELW technique also indicates that the horizontal location of the causative source is at an offset of 270 m and at a depth of 39 m. Following Smellie model, the obtained structural index of 0.5 indicates that the shape lies between the line of poles and line of dipoles. The sulfide mineralization has been localized with the integrated approach of low resistivity and high density.
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G04 Fractal and Multi-Fractal Modelling, Singularity Analysis Frits Agterberg
G0401. Fractal density and local singularity analysis - now linear mathematical geosciences theory and method for Modeling Extreme Geo-Events Qiuming Cheng The concept of fractal density proposed by the author as density of mass over fractals will be described mathematically by introducing new fractal derivative of mass over fractal. Fractal integral and differential operation are defined first and followed by discussion of their properties and implementation processes. The fractal density is demonstrated to be a fundamental property of the extreme and complex geo-processes. Several nonlinear geo-processes and extreme events occurred in the Earth’s crust originated from cascade earth dynamics (mantle convection) and selforganized criticality (slab breakoffs and faults as avalanches) will be examined from a point of view of fractal density. Examples to be examined include various extreme events caused by phase transition in the Earth’s lithosphere. These include heat flow over mid-ocean ridge,clustering frequency-depth distribution of earthquakes, magmatic flare up activities and episodic formation of super-continents caused by depth-seated mantle convection processes.
G0402. Geochemical anomaly uncertainty assessment based on stochastic simulation and local singularity analysis Yue Liu, Kefa Zhou Geochemical anomalies are directly separated into high or low anomalous zones in the study area based on one or more geochemical thresholds, which may cause some important information to be lost, or lead to faulty decision-making. From a statistical perspective, inferring the possible probability of un-sampled points or characterizing the probability distribution that the estimated values are greater or less than a certain geochemical anomaly threshold could be more reasonable
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G04. Fractal and Multi-Fractal Modelling, Singularity Analysis for actual needs of exploration geochemistry prospecting activities. For the limitations of traditional geochemical interpolation methods and geochemical anomaly evaluation, geostatistical stochastic simulation techniques and multifractal local singularity analysis were used to quantify geochemical anomaly uncertainty. Local singularity analysis was used to characterize the distribution patterns of geochemical anomaly in frequency domain by combining with percentile analysis of singularity indices, in order to attain the aim of geochemical anomaly separation. Geochemical anomaly uncertainty evaluation model is established by means of local uncertainty and spatial uncertainty algorithms which are employed to simulate uncertainty propagation processes of geochemical anomaly related to mineralization. The model was applied to a case study from the west Tianshan belt to model copper anomaly.
G0403. Lithogeochemical anomaly prospectivity mapping of HREEs using multi-fractal modeling in Saghand Area, Central Iran Masoumeh Khalajmasoumi, Behnam Sadeghi The purpose of this paper is the delineation of the geochemical anomalies of heavy rare earth elements (HREEs) lithogeochemical data, sampled from the Saghand area, Central Iran. To do so, concentration-area (C-A) fractal model, as one of the most significant techniques in geochemical anomaly recognition, was applied. In this research, because there were significantly higher values of Dy, Yb, Y and total HREEs geochemical anomalies, the rest of the elements were not taken into consideration. Based on the results obtained by C-A fractal modeling, high concentrations of Dy and HREEs are situated in the west and the center of the study area. High concentrations of Y and Yb are only located in the western part of the case study area. These anomalies occurred within the metasomatic rock units, porphyry microdioritic and acidic volcanic rocks associated with epidote alterations in the west. In the central part of the study area, the associated geochemical anomalies are related to metasomatic rock units with epidote and chlorite alterations. Very strong anomalies of Dy, Yb, Y and HREEs are respectively 630–1100 ppm, 301.9– 500.8 ppm, 2511.8–3749.3 ppm and 3467–5254 ppm. According to the correlation between geological particulars and major anomalies recognized by C-A fractal modeling, the main geochemical anomalies are located in the metasomatic rock units, chlorite and epidote alterations, the major fault in the west and the inferred fault in the central part.
G0404. Multi-fractal modeling: a significantly useful method to recognize geochemical anomalies in large-scale sampling networks Fabrizzio Sánchez, Behnam Sadeghi In this research, the main aim is to recognize geochemical anomalies in largescale sampling networks using multi-fractal modeling. As a case study, the 1:100,000 geological sheets of Chivay and Caylloma, which are located in the Central Andes, SE Peru, were selected. These two areas are mainly included by outcrops of Cenozoic volcanic rocks of intermediate composition. Besides that, there are considerable amount of hydrothermal alterations and mining prospects in the study area. In order to recognize the geochemical anomalies of the economically important metals in this area (i.e., Cu, Zn, Pb, Mn and Ni), the number-size (N-S) fractal model
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were applied on the stream sediment and litho-geochemical data. For this purpose, 273 rock samples in addition to 240 stream sediment samples were collected from the study area. The results obtained from the N-S fractal model demonstrated a multi-fractal behavior and they were validated considering the geological evidence and existing mining prospects. Comparing with the geological studies, the weak and moderate geochemical anomalies are associated with pliocene volcanic and cretacic siliciclastic rocks. In addition, the strong geochemical anomalies are recognized in the northern and southern parts of the study area with a proper overlap with the mining prospects, which are associated with oligocene volcanic rocks (lava flows and tuffs).
G0405. Multifractal Studies Using Magnitude Cumulant Analysis of Wavelet Transform: Application to Ionospheric Total Electron Content Data Shivam Bhardwaj, Enamundram Chandrasekhar, Vikram M. Gadre The present study describes the estimation of multifractal singularity spectra using cumulants of the magnitude of wavelet transform of the signal. Wavelet transform based multifractal analysis using wavelet transform modulus maxima (WTMM) method has been widely used to identify the local singular behaviour of a signal under study. While in WTMM, one needs to study the scaling behaviour of the moments (q) of wavelet transform coefficients across different scales, which leads to the estimation of the scaling exponent � to estimate the singularity spectrum, in cumulant analysis, one can arrive at the quadratic approximation of �(q) with a few linear regressions between logarithm of cumulants and logarithm of scales to determine multifractal spectra. Also, in WTMM, large number of moments are needed to do linear regression corresponding to every value of q to determine a meaningful estimate of �(q). This takes more computational time in multifractal spectrum estimation. Hence in the present work, cumulant analysis has been applied to nonlinear geophysical signals like ionospheric total electron content (TEC) data recorded during geomagnetic storm event days of the solar minimum and solar maximum years. The objectives of this study are (i) to compare the multifractal behaviour of TEC recorded during geomagnetic storm events and (ii) to understand the latitudinal dependence of the multifractal behaviour of TEC. Preliminary results show that during the geomagnetically disturbed days, TEC shows higher degree of multifractality in the years of solar maximum compared to that in solar minimum. The degree of multifractality in TEC has been observed to be highest in high latitude regions, smallest at equatorial regions and intermediary at mid latitude regions. These observations have been consistent with those obtained earlier using multifractal detrended fluctuation analysis. Interpretation of the results will be discussed in the light of above observations.
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G04. Fractal and Multi-Fractal Modelling, Singularity Analysis
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G05 Functional Data Analysis Alessandra Menafoglio
G0501. A novel inferential framework for the analysis of spatio-temporal geochemical data Veronika Římalová, Alessandra Menafoglio, Alessia Pini, Eva Fišerová In recent years the increasing need for analysing high-dimensional and complex data structures led to development of functional data analysis. In this broad framework, the aim of this contribution is to introduce a functional regression framework for modelling space-time geochemical measurements. The motivating data set includes monthly measurements of 5 chemical elements taken from 5 different sites near Brno, Czech Republic. Sampling locations were selected with the purpose of testing if the sites can be divided in two parts, agricultural and forest soil, according to their chemical properties. We suggest treating measurements as functions of time distributed in space. We explore spatial relations between measurements by means of functional geostatistical methods (e.g., trace-variography), and propose a function-on-scalar spatial regression model to describe the temporal distribution of the geochemical elements. To test for the possible differences between the two soil types, we develop a non-parametric functional testing procedure, performed on the residuals of the spatial functional model. The methodology will be demonstrated on the available geochemical dataset and geological interpretation of the results will be given.
G0502. Graph-based Spatio-temporal Clustering Using Stochastic Tree Partitioning in a Functional Data Framework Orhun Aydin, Mark Janikas, Kevin Butler Spatio-temporal clustering aims to group time series observed at different locations into spatially-contiguous regions based on the similarities of the time series. Defining contiguous regions comprised of time series with similar characteristics is essential to distinguishing different patterns in spatio-temporal phenomena such as precipitation or crime patterns. The primary challenge is to balance the traditional clustering goals of minimizing within cluster variance and maximizing between cluster variance under spatial constraints. This implies defining regions without artifacts from spatial constraints. For time series, defining compact and meaningful clusters becomes even more challenging due to lack of a dissimilarity metrics that 39
G05. Functional Data Analysis capture value and shape-related difference jointly. In this work, we address spatial compactness problem by approaching spatio-temporal clustering as a graph partitioning problem where we model spatial connectivity between objects using an edgeweighted connectivity graph. In the edge-weighted graph, every spatial location is represented as a node and the time series at each node is modeled as functional data. Edges are defined to represent neighborhood relationships between spatial locations and dissimilarity between function scores are represented as edge-weights. One of the challenges in defining regions from a weighted graph is searching for the optimal set of edges to partition the graph. We circumvent this problem by representing the weighted graph with spanning trees to search for spatio-temporal clusters. We develop a stochastic tree partitioning algorithm to find connected nodes with similar time series. Synthetic tests are designed to evaluate the quality of the resulting spatio-temporal clusters. We compare our proposed method to a widely used spatio-temporal clustering method that makes use of a joint correlation and value dissimilarity on raw time series. Finally, we apply our proposed methodology to the output of the HadCM3 global climate model to delineate global climatic regions based on modeled temperature.
G0503. Underground Mining to Processing Copper Ore Tracking Solution in DISIRE Project Leszek Jurdziak, Witold Kawalec, Robert Król An improvement of copper ore beneficiation processes aimed to decrease their specific energy consumption as well as increase of a metal recovery, depends on the proper settings of grinding/milling/flotation equipment. The actual lithological composition of the copper ore batch delivered to the mills, when recognised in advance, is considered as the key factor for the control of ore enrichment processes. However, though mining blocks are classified by geologists in-situ, ore batches that are simultaneously mined at numerous active faces of an underground mine are anonymous while conveyed and stored in ore bunkers on their long way to processing. In order to address this problem the idea of ore tracking system with the use of tags and real-time transportation system simulation has been investigated by the “Nonferrous mineral processing” work package of the DISIRE project, launched within the HORIZON 2020 framework. The project was focused on the development of the Integrated Process Control for the improvement of heavy industry processing by the implementation of Process Analyser Technology sensors embedded into the processed (transported) raw material for storing and reading of vital information. The improved identification of the ore lithological composition can be achieved by a combination of annotating the transported ore stream with pellets that keep the information about the original location of the ore (when they are dropped into the ore batch), the data of the in-situ lithology derived from the digital, orebody structural and quality block model and the real-time simulation analysis of the transportation system. The final in-situ tests of annotating the ore were supported by simulations of the ore flow with the use of dedicated simulation models of the mine transportation system. Applied simulations were developed on the basis of the real data, with regard to recognised ore flow in bunkers and validated against tests results.
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G06 Geoinformatics Mana Rahimi
G0601. A GIS-based approach to identify optimum locations of wind power plants using a multi-criteria model applied to Afghanistan Abdul Saboor Hamza, Jan C. Bongaerts, Helmut Schaeben Post war (2001) nearly stable situation in Afghanistan leads to a growing demand for energy resources. Currently, only 10% of rural population and 28% of urban households in Afghanistan have access to supply of electricity. Around 75% of Afghanistan’s current electricity consumption is imported from neighbor countries. However, Afghanistan owns a significant amount of renewable energy including wind energy resources, the theoretical potential of wind energy is estimated to produce 158 MW of electricity. This situation can change dramatically if Afghanistan is able to develop its renewable energy potential leading to substantial net exports to the region. In order to close the wide gap between the actual wind energy use and its potential, it is essential to identify the best possible locations for wind farms across the country. This paper presents a Geographic Information System (GIS) approach to recognize most suitable locations for wind farm development. Several important criteria are taken into account and combined in an integrated model. They include (physical) wind speed, topography and land coverage of the area; access to public power supply, population density, major roads, and airports. With these criteria, a multi-criteria decision supporting model is developed to identify optimum wind farm locations in Afghanistan. The case study shows that decision-makers can be provided with a GIS based approach to develop their energy policy.
G0602. Hot spot analysis of environmental variables in the big data era Chaosheng Zhang With rapidly growing databases available at regional, national, and global scales, environmental sciences are facing the challenges in the “big data” era. One of the main challenges is to find out useful information from a large volume of data, and hot spot analysis becomes a practical tool due to the requirement of spatial analysis at the “local” level. This presentation focuses on the uses of hot spot analysis techniques to reveal spatial patterns and hidden information. Spatial variation in soil geochemistry have been found at all the sizes of regional (in square kilometers), field and micro scales (in square centimeters). The techniques of hot spot analysis including local index of spatial association (LISA) and Getis Ord Gi* and their 41
G06. Geoinformatics applications are explained and investigated using examples of geochemical databases in Ireland, China and the UK. The LISA is a useful tool for identifying pollution hot spots and classifying them into spatial clusters and spatial outliers. The Getis Ord Gi* is effective in identifying spatial clusters of both hot spots and cool spots. Both are useful tools to identify spatial patterns of high and low values and to reveal hidden information of spatial patterns which are helpful to pinpoint areas of interest with special features, providing useful information for mineral exploration, environmental management and agriculture. Meanwhile, new opportunities have arisen from the current concept of “big data”, but the challenges for more effective “data analytics” are emerging.
G0603. Towards a web-based information system for multi-physics detector data Florian Bachmann, Mario Hopfner, Heinrich Jasper, Helmut Schaeben, Björn Wieczoreck Multi-physics detector experiments delivering spatially resolving multi-pa- rameter datasets resulting from different electro-magnetic interactions at various depths and volumes are quite common in geo- and material sci- ences, e.g., low-energy electron diffraction (LEED), cathodoluminescence spectroscopy (CLS), energy dispersive X-ray spectroscopy (EDX), or elec- tron back scatter diffraction (EBSD). A joint view or dynamically linked views of several map images of signals from different experiments require a unique spatial reference to allow for a common interpretation. Then new re- lationships of these data will enrich the interpretation of the physical, chem- ical and defect state of specimen. To discover these mutual dependencies requires a systematic organization of these datasets, in particular of those referring to the same specimen, in a database and appropriate tools for data mining as provided by information systems. As a first step towards such an information system, a database-scheme has been designed and implemented to store and manage multiply spatially referenced EBSD data as well as all relevant meta data, i.e., a comprehensive documentation of a typical EBSD experiment. This extensive, database-centric approach to archiving experi- mental results allows for convenient and customizable data querying. Since all data are spatially referenced, plotting corresponding map images is of immediate interest. Therefore, an abstract web service has been designed, prototypically implemented, and applied to exemplify the visualization of map images of crystallographic orientations from EBSD measurements with a web service.
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G07 Geostatistics Peter Dowd, Ute Mueller
G0701. A Pareto Approach to the Construction of an Optimal Space-filling Design Christien Thiart, Kago Kebotsamang, Linda Haines Our research problem originated from a collaboration with the Earth Stewardship Science Research Institute at Nelson Mandela University (AEON-ESSRI). The Institute had assembled a hydrocensus database comprising the sites of N = 756 boreholes and springs. For each site, the location, that is the latitude and longitude, and ancillary information on electrical conductivity (EC), depth and the quaternary catchment coverage area are included but no prior data relating to the hydrochemistry was available. To compound matters, the geologists did not have the resources to measure groundwater quality at all the boreholes. An immediate research question is: What is the spatial configuration of a subset of the boreholes and springs which will produce a “best” sample for monitoring baseline water parameters? Space-filling designs are optimal or near-optimal with respect to distancebased criteria and these criteria can be combined to form the basis for a multicriteria approach to borehole selection. Various problems arise however. Specifically, the optimality criteria must be transformed to a common scale, a weighting scheme for combining criteria is required but can be interpreted as being subjective and the trade-off between competing criteria is not clear. A more flexible and robust approach to design construction which is rooted in Pareto optimality is therefore proposed here. In this approach, a Pareto front comprising candidate solutions for which no solution is better than any other in terms of all the competing criteria is generated. The decision-maker can then evaluate possible trade-offs between criteria, investigate the robustness of candidate solutions to different individual preferences and select a design appropriate to his or her needs.
G0702. Anisotropic Kernel Function based Geographically Weighted Regression for Mineral Exploration Jie Zhao, Wenlei Wang Anisotropy, complexity and spatial non-stationarity are essential characteristics of non-linear geological processes. This research is intent to introduce geographically weighted models which were frequently utilized in social economics to the study of mineral exploration, by which non-stationarity of mineralization process can be identified. The kernel functions generally embedded in geographically weighted models
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G07. Geostatistics include ordinary least square, Gaussian, Boxcar, Bisquare, etc. Within those kernels, bandwidths were set to be circular shape no matter it is fixed or adaptive. Standing on the point of non-linear theory, these kernels with circular-shape bandwidth projection are thought to be isotropic, which are not objective in reflecting spatial distributions of the anisotropic geological processes and their products. The current research is carried out in eastern Tianshan mineral district China. The submarine volcanic-sedimentary iron deposit is one of the main mineralization types in this area. The formation of the deposits is mainly associated with the Early Carboniferous volcanism, tectonic structures, and post-mineralization hydrothermal activities, etc. The effects of each controlling factors at different locations are believed to be different in spatial, temporal, and degrees. For example, the extent of hydrothermal enhancement of mineralization is obviously related to tectonic settings which are generally oriented. Thus an isotropic kernel function with circular-shape bandwidth would not be suitable in depicting the phenomenon which is related to fractures. The current research is about to develop anisotropic kernel functions reflecting geological realities and propose anisotropic kernel function based geographically weighted regression model which will be applied in quantitative estimation of spatial anisotropies of ore-causing anomalies, as well as metallogenic prediction. This research means significantly in elaborate description of non-linear characteristics of complex geological processes, development of non-linear theories of mineral resource prediction, and enhancement of authenticity and accuracy of predicting results.
G0703. Building a continue spatial variation grid from an isovalue map Jean-Michel Metivier Very often, we only have a map with isovalue lines, the data set associated with this map is seldom provided, forgotten or even lost. However, one intends to have the variation from a grid ! One solution is to digitize isovalue lines of the map and to take a value on them for a constant distance or not. The point pattern values are then interpolated by a deterministic or probabilistic method. The main disadvantage is that the high or low values (”trough or peak”) are not available. A second method consists to map the variability within each polygons using ATP kriging (Area To point kriging) from SpaceStat v2015. Thus, it is possible to conduct a deconvolution of the areal or regularized variogram and infer the pointsupport variogram model. To reduce computational time, a discretization option may be selected. The selection of areal data can either be based on spatial weight sets (e.g. queen or rock neighbors) or on distance between centroids of the polygons. The last operation allows to verify the pycnophylactic* property. In the case of Area-To-Point kriging, it means that averaging the kriging estimates within each polygons should return the original areal data. The interpolation method is ‘average/sum’ and the weight is normalized. A scatterplot of ‘kriging estimate two means’ versus the original areal data ‘kriging estimate’ verifies that indeed these two datasets are the same (perfect correlation, same mean and variance). Two examples will be shown, the spatial distributions of caesium-137 deposits that have lasting contaminated territories in Ukraine (Chernobyl nuclear accident) and in Japan (Fukushima Daiichi nuclear accident).
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G0704. Geostatistics for Geometallurgical Property Prediction K. Gerald van den Boogaart, Peter Menzel, Kai Bachmann, Nataliia Krupko, Angel Prior, Raimon Tolosana-Delgado, Jens Gutzmer Geometallurgy distinguishes primary and secondary ore properties. Primary properties are describing the analytically observable properties of the ore, such as chemical composition, modal mineralogy or microstructure. Secondary properties describe the properties observable in processing experiments, like milling energy consumption, recovery and concentrate grade and thus finally monetary return as a function of processing decisions. While only the primary properties can be obtained in a sufficiently dense spatial grid at reasonable costs, the secondary properties determine processing decisions and the ultimate value. The standard approach is to estimate two things: A geostatistical block model of primary properties based on a spatial dataset and a regression model mapping the observable primary properties to experimental secondary properties on a smaller dataset. This regression model is then applied to the predicted primary properties. Due to the nonlinearity of the dependence and due to the difference in covariance structure between observed and predicted primary properties this is however misleads to suboptimal processing decisions and a lower resource and energy efficiency. We solve this problem by defining intermediate properties, which can be computed directly from automated mineralogy data, on which processing properties depend approximately linearily. In this approach each particle observed is separated, remilled and circulated in virtual plants according to their observed microstructures. Nonlinear effects like the influence of the microstructure on the recovery are handled in this transformation. In this way most value relevant properties (recovery, dillution and mass pull, etc.) become a linear function of the virtual distribution of microstructures. This multivariate dataset of intermediate values is then predicted by standard geostatistics. The methodology is exemplified with a 2D case study from a chromite mine based on local geological knowledge and automated mineralogy data acquired on a suite of samples from known locations. The resultant model readily illustrates domains of differing processing characteristics.
G0705. High-Order, Data-Driven Categorical Simulation and Applications to Mineral Deposits Ilnur Minniakhmetov, Roussos Dimitrakopoulos In today’s competitive mineral supply environment, categorical spatial simulation is often required in resource estimation of mineral deposits to describe alteration, lithology, mineralization zones and related spatial and volumetric uncertainty. Existing approaches in the mining industry is to use variogram-based or second-order methods. However, such methods fail to describe complex geological structures and preserve contacts between categories. Multi-point statistical methods were introduced in 90’s in the petroleum industry to overcome these limitations based on geological analogues or training images. Although the concepts are relevant, training images do not comply with spatial statistics of hard data that is orders of magnitude denser when modelling mineral deposits, as opposed to petroleum reservoirs, thus conflicts are generated. Recently, a high-order datadriven simulation approach based upon the approximation of high-order spatial indicator moments has been developed. This approach does not require a training image and adopts the complexity of the model to the information available in the 45
G07. Geostatistics available drilling data. This paper presents a three-dimensional generalization of the algorithm and its application to several mining deposits. The advantages of the proposed approach include the ability to reproduce complex spatial relations in the data available, contacts between categories and honor multiple high-order cross-categorical spatial relations. These relations are quantified and analyzed using high-order indicator cross-moments. The approach is tested with data from a copper-porphyry and a gold deposit and show good reproduction of continuity, contacts, and high-order cross-categorical spatial relations.
G0706. Three-dimensional stochastic modeling framework for Quaternary sedimentary structures using multiple-point statistics Qiyu Chen, Gang Liu, Gregoire Mariethoz, Xiaogang Ma Multiple-point statistics (MPS) has shown promise in representing complicated subsurface structures. The Quaternary sedimentary system possesses prominent anisotropy characteristics such as loose sediments, uneven distribution, and thinner strata and so on. For a practical three-dimensional (3-D) application, therefore, the critical challenges are not only from the difficulty to obtain a credible 3-D training image, but also from the serious non-stationarity. Moreover, MPS-based simulations are usually performed on a regular Cartesian grid. Such models cannot realistically reflect the actual shape and distribution characteristics of geological structures. In this work, an integrated 3-D MPS modeling framework is presented according to the characteristics of Quaternary sedimentary structures and the distribution features of the dataset obtained from Quaternary geological exploration. First, the 3-D modeling grid is constructed based on the constraints of faults, folds, terrains and large-scale stratigraphic frameworks, and then the informed data are assigned to the 3-D grid. Second, the mapping relationship between the 3-D modeling grid and the simulation grid is established. Third, three MPS implementations are used to reconstruct entire 3-D models by using partial 2-D training images, and the performance of the three MPS approaches are discussed. Finally, the results are embedded into the 3-D modeling grid so as to achieve more realistic visualization for the subsurface structures. An actual 3-D modeling practice is implemented on the basis of the dataset from a Quaternary geological and environmental survey project of a southeast coastal city of China. Compared to the actual geological background, the geological meaning and deep-seated statistical features are revealed and analyzed. The real 3-D application verifies rationality and applicability of the presented workflow where the various structural features, such as faults, folds, terrains and the results of MPS simulations, are integrated into a unified 3-D model to achieve a more realistic visualization.
G0708. Probabilistic assessment of in-place coal tonnage for public disclosure of mineral resources Ricardo A. Olea, Jon E. Haacke, Brian N. Shaffer, James A. Luppens Internationally, public disclosure of mineral resource magnitudes and associated uncertainties is accomplished primarily following nearest neighbor approaches that have become outdated due to the availability of superior tools in risk analysis and mining geostatistics. The new assessment methodology in this study builds on kriging and stochastic simulation for a probabilistic assessment of total in situ tonnage as well as detailed mapping of tonnage and its uncertainty at the computation node 46
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level. The methodology is robust and general enough for a wide application in all mining, but its application in this study is focused on the assessment of coal resources. Conventional mathematical methods are supplemented with knowledge specific to the geology of each deposit. Coal bed realizations are post-processed for assuring that thickness converges to zero in all areas where the assessor postulates that the coal bed pinches out. In addition to the geometry of sampling, this assessment methodology takes into account uncertainty in coal bed boundaries, complexity in geology, and fluctuations in coal density within a bed. The methodology is illustrated with an application to a coal bed in the Little Snake River coal field, Greater Green River Basin, Wyoming, United States.
G0709. Quantile Sampling: a new approach for multiple-point statistics simulation Mathieu Gravey, Gregoire Mariethoz Developments in multiple-point statistics (MPS) algorithms over the last decade have made the techniques more and more viable for the simulation of earth models. However, a recurrent issue with these approaches is their difficult parameterization. In most cases, finding an appropriate set of algorithmic parameters consists of identifying an optimal trade-off between simulation quality and computational cost. In some cases, parameters are extremely sensitive, making it difficult to find such tradeoff. The situation can be even more complex when the TI is incomplete or contains continuous data. Here, we propose Quantile Sampling (QS) a new MPS approach originally designed as an efficient pixel-based simulation algorithm for continuous or categorical variables. QS is easy to parametrize, robust to non-stationary data and has a predictable simulation runtime, that is almost independent of algorithmic parameters. It can handle complete or incomplete training data, for univariate or multivariate unconditional and conditional simulations. We provide a free, flexible, reusable, parallelized, and highly optimized C/C++ implementation. The code is portable for any platform able to compute FFTs, such as GPU (provided through OpenCL) or FPGA (Field-Programmable Gate Array: configurable circuits that dramatically reduce power consumption). In addition, we provide a server-client interface to easily communicate with MATLAB or Python. The code is released under the GNU GPLv3 License, such that anyone can use, customize, and improve it.
G0710. Simulation of the ore boundaries of a lateritic bauxite deposit using multiple-point statistics Yasin Dagasan, Philippe Renard, Julien Straubhaar, Oktay Erten, Erkan Topal Modelling the orebody boundaries is one of the tasks to create resource models for lateritic bauxite deposits. This is frequently performed utilising sparsely spaced boreholes which are drilled on a regular grid. Given the geostatistical modelling techniques, the structural information is modelled using the experimental variogram of the borehole data. Since the borehole spacing chosen often cannot capture the lateral variability of the geological contact, the structural variations inherent in the contact cannot be adequately inferred. Therefore, the created models cannot represent the natural variability of the contact topography. Multiple-point statistics (MPS) can be used to overcome such limitations of the traditional geostatistics using training images (TI). Rather than inferring the structural information using the 47
G07. Geostatistics collected samples, the TI provides a rich and complete information on the geological structures. In this study, one of the MPS algorithms, Direct Sampling (DS), is used to investigate the applicability of MPS to simulate the orebody boundaries for a lateritic bauxite deposit. The chosen TI comprises the Ground Penetrating Radar survey and the mined-out topography of a historic mine area. The comparison made with the Turning Bands method suggest that the DS can satisfactorily model the orebody boundaries using previously mined out areas as a prior information.
G0711. Spatial estimation of daily rainfall over a number of years with small data sets Peter Dowd, Eulogio Pardo-Igúzquiza Estimated daily rainfall over a number of years is a critical input into the estimation of aquifer recharge. In many cases, the aquifer of interest is on a regional scale (up to a few hundreds of square kilometres) and often there are relatively few rain gauges. We present examples with less than 20 rain gauges and an extreme case in which only three rain gauges were available. In such cases, meaningful estimates of rainfall require the inclusion of secondary information to make up for the sparsity of the primary data. In the absence of radar or satellite estimates of rainfall, the topography, in the form of a digital elevation model, is the most often used secondary data for daily rainfall estimation. The combination of sparse primary data and the significance of the secondary data raises two questions: how best to include the secondary data (e.g., cokriging, kriging with an external drift, regression kriging) and how to choose the most appropriate temporal scale on which to model variability (e.g., daily variogram, monthly variogram, annual variogram). In this work we discuss the problems raised by daily rainfall estimation for long sequences of days and we compare a number of solutions together with their advantages and disadvantages.
G0712. Kriging for tensor data through Object Oriented Spatial Statistics Alessandra Menafoglio, Davide Pigoli, Piercesare Secchi The increasing availability of spatial complex data has fostered the development of Object Oriented Spatial Statistics (O2S2), an innovative system of ideas and methods that allows for the analysis of general types of data when their spatial dependence is an important issue. The foundational idea of O2S2 is to interpret data as objects: the atom of the geostatistical analysis is the entire object, which is seen as an indivisible unit rather than a collection of features. In this view, the observations are interpreted as random points within a space of objects – called feature space – whose dimensionality and geometry should properly represent the data features and their possible constraints. In this communication, we focus on the problem of analyzing a set of spatial tensor data. These are georeferenced data whose feature space is a Riemannian manifold. Riemannian manifolds are non-Euclidean spaces, which can be locally approximated through a Hilbert space. In this setting, the linear geostatistics paradigm cannot be directly applied, as the feature space is not close with respect to the Euclidean geometry (e.g., a linear combination of elements in the manifold does not necessarily belong to the manifold). We shall discuss the use of a system of tangent space approximations to locally describe the manifold through linear spaces, where the linear object-oriented methods can be applied. Here, we develop estimation methods and a consistent Kriging technique
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for tensor data. Although the presented approach is completely general, for illustrative purposes we will give emphasis to the case of positive definite matrices. The latter find application in the analysis and prediction of measures of association, such as the covariance matrices between temperature and precipitation measured in the Quebec region of Canada which are used to illustrate the methodological developments.
G0713. Virtual mineral processing simulation in software MLALookUP Nataliia Krupko, Marius Kern, K. Gerald van den Boogaart Designing a more effective and productive mineral processing plant is a major objective for engineers and researchers. An optimized flowsheet produces one or more concentrates with high recovery and grade of the target mineral(s) and low impurities of minerals that reduce the value of the concentrate. In the initial stages of flowsheet development, lab-scale experiments are prepared and meticulously reviewed. This process is very time-consuming and cost-intensive. Furthermore, the results of these experiments can be inconclusive. To overcome these problems, a virtual mineral processing simulation software called MLALookUP was developed. The simulation model helps to predict the performance of a processing plant and to find the optimal order of processing techniques to reach the targeted concentrate composition. MLALookUP uses data from mineral liberation analysis (MLA), a tool that generates and analyses high-resolution images with compositional particle information by combining scanning electron microscopy and energy-dispersive X-ray spectroscopy. The software uses geometallurgical properties of the material that was analyzed with MLA. Depending on these properties, MLALookUP runs virtual separation machines, which are prepared and analyzed by the user on the basis of threshold parameters. Starting with the feed material, a sequence of virtual separation machines simulates all processing steps until the final concentrate. In this way, the values of grade, recovery and mass proportion are predicted in each stream. The software gives the possibility to vary processing threshold parameters and to define the optimal order of processing experiments in a flowsheet.
G0714. Wall scale analysis of weathering feature distribution across sandstone facades Brian Johnston, Jennifer McKinley, Patricia Warke Sandstone buildings within the urban environment are subjected to accelerated processes of weathering resulting from the presence of salts and pollutants. Investigations of these processes have historically been reductionist in nature or have focused upon spatial patterns at the block scale. This study aims to investigate deterioration across the wall scale using permeability measurements. Data was collected across the surface of a historic sandstone wall in the city of Belfast using a probe permeameter. This data was provided with positional information using a three-dimensional model produced using a 3D scanner. Analysis of this data, using variography, provides insight into the spatial distribution of permeability resulting from construction elements and weathering features. Though the subsequent use of categorical simulation, applied as part of an integrated approach with geomorphometric analysis, it is possible to develop improved understanding of surface properties resulting from emplacement within the urban environment.
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G07. Geostatistics These findings support the development of conceptual models of urban weathering processes and discussions of the influence of microclimatic influences.
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G08 Machine Learning, Pattern Recognition, Data Mining, Big Data Silversides Katherine
G0801. Prospectivity modeling incorporating spatial dependencies through convolutional neural networks Samuel Kost, Georg Semmler Prospectivity modeling is a method used since decades to predict undiscovered deposits. Over the years several different methods were developed. These include for example weights of evidence, logistic regression and with the dawn of machine learning artificial neural networks. The problem to solve is the prediction of locations for which the conditional expectation of the occurrence of a target event is maximum. Independent of spatial dimension, all methods operate on a regular grid ignoring the spatial dependencies between the grid cells. It was shown that reordering those cells will not change the result for each cell. Since geology includes spatial relations by definition, solutions have been proposed to tackle this problem. The most popular one is to pre-calculate possible interesting spatial relations and use them as additional covariables. Obviously it is not possible to pre-calculate all spatial relations in a dataset with a sufficient large number of variables. Therefore a user needs to decide which dependencies should be pre-calculated. This introduces a systematic error because a user tends to choose obvious candidates. A spatial dependency could be mathematically modeled as convolution which immediately suggests the use of convolutional networks. Therefore we introduce a new method based on convolutional neuronal networks to incorporate spatial dependencies. Besides combining different covariables to predict the final target variable, our model also uses the covariables from the neighbourhood of the current cell. Using this approach we can infer the general spatial dependency of the target variable for the model given the covariables of the surrounding neighbourhood directly while building the model. In return this method requires the explicit handling of the boundaries of our prediction area. This concept is independent of the number of dimensions. The performance of the new approach is demonstrated on an artificial test dataset containing strong spatial dependencies.
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G08. Machine Learning, Pattern Recognition, Data Mining, Big Data
G0802. Can boosting boost exploration targeting? Melanie Brandmeier, Irving Cabrera, Vesa Nykänen, Maarit Middleton With an increasing demand for critical raw materials, good predictive models that allow minimal invasive exploration are of great importance. We evaluated different machine learning techniques with an emphasis on boosting algorithms and implemented them in a toolbox for ArcGIS. Performance was tested on real exploration datasets with respect to accuracy, performance, stability and robustness. Our test area is the Iberian Pyrite Belt (IPB), one of the oldest mining districts in the world that hosts giant and supergiant massive sulphide deposits. The spatial density of ore deposits, the size and tonnage make the area quite unique and due to the available data and number of known deposits well-suited for our purpose. We combined different geophysical datasets as well as layers derived from geological maps like distance to faults or lithological units as predictors. Different algorithms were tested and compared to Adaboost in several experiments (variation of the train/test ratio, using data augmentation). We found performance results relatively similar for the machine-learning algorithms with boosting (especially BrownBoost) slightly outperforming logistic regression and SVM. Data augmentation led to improved results by around 5% in this setting. Variations in the split ratio lead, as expected, to a reduction in accuracy but we observe relative stability over until a critical point (ca. 90 train deposits out of 350 total deposits) and dropping rapidly from 26 deposits downward. In comparison to other machine learning methods, adaboost is easy to use because of relatively short training and prediction times, higher resistance to overfitting and the user is required to tune only two parameters. Furthermore, it allows working with continuous datasets unlike widely used methods in prospectivity mapping, e.g. Weights of Evidence. In the preliminary results, the Adaboost algorithm shows high accuracy in real datasets, making it an excellent data-driven alternative for prospectivity mapping.
G0803. Fusing traditional and contemporary modelling approach in multi-scale mineral potential studies Soile Aatos, Eevaliisa Laine, Mikko Kolehmainen Project GECCO combines expertise in high performance computing and geomodelling, and aims at developing tools for faster geological common earth modelling (CEM) in a powerful computing environment. In the project, traditional geochemical CIPW norm calculation was used as a pre-processing step in applying more modern clustering and neural network techniques to enhance machine-based identification and feature extraction procedure of bedrock units having or constraining metal potential or risk. These results were integrated and visualized in 3D modelling tools with structural geomodelling results of the project. The input compositional data matrix consisted of 4846 rows and 23 columns of lithogeochemical drill core data from closed Mullikkoräme massive volcanic zinc sulphide mine, Finland. CIPW pseudo-mineralogy was hypothesized to reduce the non-linear characteristics of compositional element and oxide distributions of a large lithogeochemical data matrix, and form conceptually a more recognizable and geologically a more consistent interpretation schema compared to statistical dimension reduction approaches, like linear PCA. After CIPW calculations, unsupervised kmeans clustering and MLP neural networks were used in identifying the characteristic features of hydrothermal metal enriching alteration processes intertwined with 52
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the magmatism of Mullikkoräme mineral system, and in predicting the Mullikkoräme type metal potential or risk in the surroundings of the exhausted deposit. The clustering results were visualized in the Mullikkoräme geological 3D model showing the main lithological boundaries and discontinuity structures. In order to compare the bedrock lithologies and mineralizations against unsupervised clustering, MLP predicted values were interpolated using kriging interpolation. A correspondence between kriged MLP values and Mullikkoräme mineralizations was found. Preliminary statistical examination of the clustered CIPW results supported the idea of enhancing geological feature identification in large lithogeochemical data with computational aids. Statistical validation of the preliminary MLP results indicated that computed features may be of use in predicting metal incidences in the data.
G0804. A non-destructive measuring method for rock strength via hammering sound Shuai Han, Heng Li, Mingchao Li, Qiubing Ren Hammering different strength rocks can make different sounds. In this way, geologists usually determine the strength of rocks roughly in geology survey. This method is quick and convenient, but obviously subjective. Inspired by this problem, we present a new non-destructive measuring method for surface strength of rocks based on transfer learning and spectrum analysis. One of the most advanced deep learning network for image classification, Inception-ResNet-v2, is fine-tuned to extract the features of the spectra that collected through hammering rock. Besides, a selection method for training samples is presented based on clustering algorithm. Training result shows the model can reach a accuracy of 94.5%. After that, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) are adopted respectively to fit the relationship between the features of the spectra and their corresponding strength values. The finally validations show that KNN has the highest fitting accuracy, and SVM has the strongest generation ability. Overall, the proposed method is of great potential in underpinning the implementation of efficient rock strength measurement in the field.
G0805. Reconstruction of vugular carbonate rocks by pore network modeling and image-based network technique Saeid Sadeghnejad, Jeff Gostick Carbonate rocks have often a complicated flow behavior due to their complex pore-structure. A problem in modeling of these rocks is dealing with the multiple spatial scales inherent to the availability of vugs. These vugs can alter flow paths because of creation of a multi-modal porosity system with different interconnectivity at pore scale. This behavior can significantly affect rock’s transport properties (e.g., porosity, permeability, capillary pressure, etc.). In this study, a new computer-modeling algorithm to reconstruct bi-modal vuggy porous medium is introduced by coupling pore-network modeling approach with image-based network techniques. This approach implements image-processing techniques to generate a two-dimensional lattice-based network of secondary porosity (i.e., vugs) on top of an initial pore network model at pore scale. The resulting multi-scale model can efficiently preserve vug-to-vug and vug-to-pore connectivity through generating throats
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G08. Machine Learning, Pattern Recognition, Data Mining, Big Data with proper size and length. Vugs are allowed to overlap each other and the primary and secondary porosity of the generated network can be calculated by image processing. Modifying the effective conductance of the overlapped vugs enables the pore network model to calculate the permeability of the dual porosity network by applying mass conservation and Poiseuille law at pores and throats, respectively. This model can be a tool of linking the rock microstructure to petrophysical properties at higher scales. The model is developed in Python using SciPy vectorization technique and OpenPNM has been implemented as a general tool for pore network modeling. Simultaneously generating vugs as well as efficient pore/throat connectivity handling of the dual porosity system enables us to generate thousands of vuggs in a fraction of second on rather large-sized porous media. To examine the proposed model, petrophysical evaluation is performed based on the stochastic models acquired form mercury intrusion pore size distribution of carbonated plug samples from a middle-east oil reservoir.
G0806. Comparing linear regression and Gaussian Processes approaches to approximate mineral group densities in an iron ore deposit Mehala Balamurali, Katherine L Silversides, Arman Melkumyan Mineral deposits rarely contain large volumes of pure minerals. This can make it difficult to determine representative values for properties such as density, which is required for geological modelling and mine planning. This study aimed to investigate the applicability of linear regression (LR) and Gaussian Processes (GPs) models to determine mineral densities from mixed samples. Our test deposit is a typical Marra Mamba style banded iron formation (BIF) hosted deposit in the Hamersley Region of Western Australia. The deposit contains twelve main minerals or mineral groups that are manually logged in rock samples. For example, BIF is considered a single mineral group, although it contains both silica and iron oxides. The standard method for estimating density in deposit is to linearly add the densities for the logged minerals. To determine the representative densities, the relationship between the predictors (minerals groups) and the response variable (mixed sample density) must be mathematically described. LR analysis generates an equation with regression coefficients that describe the statistical relationship between these variables. GPs train a model to fit data by learning a hyperparameter for each variable and provide non-linear and probabilistic results. Existing densities were only available for nine of the mineral groups. The LR predicted densities had errors from 2.7 to 45.8%, with a median of 10.0%. For the GP the range was 2.5 to 49.6%, and the median 7.0%. The large variation in accuracies was related to the occurrence of each mineral, with the rarer mineral groups having lower accuracies. One mineral was an extreme outlier for both methods. The GP was generally more accurate than the LR, and also provides an estimate of uncertainty that can be used to determine which results are more likely to be inaccurate. Therefore GPs can provide approximate density values when direct measurements are not available.
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G0807. Distributed Indexing Technique for Timelines He Zhenwen, Xiaogang Ma Timelines have been used for centuries and become more and more widely used with the development of social media in recent years. A large number of smart phones and some other internet of things generate massive data related to time every day. Most of these data can be managed in the way of timelines. However, the storages and queries processing big timeline data effectively and efficiently is still a challenge. The majorities of existing studies have focused on indexing spatial dataset and interval dataset rather than the timeline dataset. In addition, most of them are designed for a centralized system. How to design a timeline index structure adapting to parallel and distributed computation framework is in urgent need of research. Therefore, we have developed a novel timeline index in the distributed system called Distributed Triangle Increment Tree (DTI-Tree). The DTI-Tree consists of one T-Tree and one or more TI-Trees based on a triangle increment partition strategy with Apache Spark. It is a master/slave architecture implemented by two interfaces MasterProtocol and SlaveProtocol based on Hadoop RPC. Furthermore, we have provided an open source timeline benchmark data generator named TimelineGenerator to generate various timeline test datasets for different conditions. The experiments for DTI-Tree’s construction, insertion, deletion and similarity queries have been executed under a cluster with some benchmark datasets generated by TimelineGenerator. The experimental results show that the DTI-tree provides an effective and efficient distributed index solution to big timeline data.
G0808. Enhancement of unconventional oil and gas production forecasting using mechanistic-statistical modeling Justin B. Montgomery, Francis M. O’Sullivan Unconventional oil and gas are playing an increasingly important role in energy markets but the physical drivers determining well productivity are poorly understood and production forecasting remains an ill-posed problem. Currently, unconventional production forecasting is carried out using individually-fitted or field-averaged production decline curves which are unable to rigorously incorporate information from offset wells while systematically controlling for physical differences between wells. As a result, this approach can significantly over- or under-state uncertainty and bias predictions. However, there is now an abundance of production, completion (e.g. hydraulic fracturing), and geological data from unconventional fields with thousands of wells; this data provides substantial insight into patterns of productivity. Improving the accuracy of well productivity predictions by leveraging these disparate sources of information requires new tools which can combine the statistical power of large datasets with knowledge about the mechanisms governing the system. Here we present a mechanistic-statistical approach to production forecasting using hierarchical Bayesian modeling, in which a hierarchy of simple models structured by physical relationships are used to generate samples from the joint probability distribution of all parameters in the system. A non-dimensionalized solution to a one-dimensional flow problem with planar fractures is used as the empirical production model and physical basis to relate temporal and spatial productivity patterns to properties of the stimulated reservoir volume. This linkage allows geological and completions data to be effectively integrated with production data, improving prediction accuracy and contributing to a better understanding of 55
G08. Machine Learning, Pattern Recognition, Data Mining, Big Data the value of different well designs and well placement strategies. We present results from applying this approach to a large public dataset of unconventional wells using cross-validation to evaluate model performance.
G0809. Fast and robust probabilistic classification method for fracture identification in BIG seismic datasets Egbadon Udegbe, Eugene C Morgan, Sanjay Srinivasan Motivated by techniques in real-time image object detection, we present a fast and robust data analytic tool for automated fracture identification in BIG poststack seismic datasets. The proposed algorithm computes time-space amplitude statistics using Haar-like bases as input to a probabilistic classification framework. These simple-to-calculate features enhance computational efficiency and eliminate the need for expensive seismic attribute computation in existing workflows. Firstly, we generate multiple post-stack amplitude seismograms with known discrete fracture location, length and orientation, by simulating elastic wave propagation in fractured media. Next, we sample amplitudes from fracture and non-fracture locations, using a spatio-temporal search window that optimizes on resolution, scan times, and discriminative performance. The amplitude content in these training windows are then characterized using image statistics based on Haar filters, which can be computed rapidly. Finally, using these amplitude-based features, we train a cascade of boosted binary classification tree models. Adapted from real-time face detection, this cascade approach further maximizes efficiency by quickly weeding out obvious non-fracture regions, and reserving complex computation for more subtle differences. The proposed approach has been tested using amplitude seismograms with discrete macro-fractures of known location, length and orientation. The results show classification accuracy of over 92% and negligible false positive rates. The classifier also outputs a time-space distribution of fracture probability, which clearly delineates individual fractures and shows excellent agreement with known fracture information. These result demonstrate the viability of the proposed amplitude-based classification approach for identifying fractures in post-stack seismic data, which offers improved computational efficiency over traditional seismic attribute-based schemes. Additionally, Haar-like features provide better scalability and discriminative performance than raw amplitude samples, which are used as input to existing deeplearning classification models. Overall, this tool would help to speed up interpretation of massive 3D/4D seismic datasets in fractured reservoirs, to better assist reservoir management and decision-making.
G0810. Feature Extraction from Spatial Fields Sean A. McKenna Identifying the relationships between the inputs and outputs of a function is a fundamental aspect of machine/statistical learning and artificial intelligence techniques. When the inputs take the form of a multivariate random field, identifying the specific features in the field that have the most significant impact on the outputs requires tools that can operate on multivariate fields and incorporate any spatial correlation. A critical question is determining the sensitivity of the output to different areas of the spatial field. Areas of high sensitivity are considered features of
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the spatial field important to creating the output and may be targeted for further characterization. Resources applied to understanding regions to which the output is relatively insensitive can be decreased. Two approaches to spatial feature extraction are examined: 1) Treating each cell/pixel in the multivariate field as an independent variable, applying a statistical test in a pixel-wise manner to the strength of the input and output relationships and displaying a map of the strength of the relationship. Areas with strong input-output relationships are considered important features; 2) Extending the steps in the first approach to create a final map of the strength of the relationships that is not just a collection of independent pixel-wise assessments, which are qualitatively grouped by their proximity in space, but producing a final map that is a multivariate random field to which hypothesis testing can be applied. These techniques are compared on two examples demonstrating linear and non-linear integration of the input variables: vegetation growth as measured in satellite imagery; and pressure response due to pumping in a heterogeneous ground water aquifer. These examples are used in both classification and regression problems to demonstrate identification and extraction of features important to the input-output relationship.
G0811. Classification for Small and Unbalanced Hyperspectral Image Based on Generative Adversarial Networks Kang Wu, Zhaoying Yang, Wang Yao, Jin Qin, Ying Zhan, Ying Cao, Yuntao Wang, Xianchuan Yu Small quantity and great imbalance is a challenge for the research of Hyperspectral image. The traditional classification method cannot work well under that challenge. This paper aims to give the model with generalization ability, which can be trained to generate more balanced Hyperspectral data with less training data. The generative adversarial networks (GANs) is a state of art to do the generation work and achieve the semi-supervised learning. Inspired by that, we apply the GANs to the 1-D Hyperspectral data. 1) We balance the samples with the generation from the latter half of the trained model, which is relatively stable and meaningful. 2) To improve the generalization ability of the model, we add the noise of random normal distribution to the generator and weaken classifier to improve the accuracy. 3) Against the problem of instability and slow convergence in GANs, we train the generator alone using the spectral angle as the loss function in every epoch instead of guiding the generator with the discriminator. For the experiments, first, we choose 20% of the data set Pavia University to test our method. Comparing with the traditional classification method such as CNN, the results show that our method can achieve higher accuracy in classification with the same number of training data, even it is unbalanced. Second, we do the experiment to show how the scale of the synthetic Hyperspectral data effects the classification. The results show the effectiveness of the model we proposed.
G0812. Improved Well Placement Optimization Procedure Using Geomechanical Constraints and Machine Learning Gaetan Bardy, Jeffrey Yarus, Shohreh Amini, Harold Walters, Steven Drinovsky While most well placement optimization techniques only consider petrophysics
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G08. Machine Learning, Pattern Recognition, Data Mining, Big Data and dynamic properties as key parameters of recovery, we are proposing, here, to integrate geomechanical parameters because they directly impact final recovery and well costs. Additional focus is on shale reservoirs where hydraulic fracturing is used, which increases the importance of geomechanical parameters, such as stress and strain. A new workflow was developed considering a numerical reservoir model with a natural fracture network. First, the porosity and permeability were computed using sequential Gaussian simulation. Then, the local variation of the regional stress according to the fracture network was determined using a material point method based algorithm. These parameters are combined to target areas with higher porosity, higher permeability, but low differential stress. In these areas, the strain inside the reservoir for a given well configuration is finally computed. This approach enables determining the hydraulic fracture propagation potential and then the corresponding recovery using a numerical flow simulator. Consequently, the best configuration for a given number of wells can be identified. The preliminary results show that this workflow enables obtaining a significant gain in terms of total recovery compared to a classical well position optimization procedure, and it also guarantees that the proposed locations are feasible because the wellbore stability parameter is included in this optimization.
G0813. Automatic fission track recognition and measurement in 3D Alexandre Fioravante de Siqueira, Sandro Guedes The fission track dating (FTD) is based on the spontaneous fission of 238U, an impurity in natural minerals. The fission process releases two fragments that trigger the displacement of atoms, leading to a structural net modification called latent track. After a convenient etching process, channels are formed along the latent track trajectory and become visible under an optical microscope. Procedures for measuring and counting tracks are time-consuming and involve practical problems. An automatic method based on image processing techniques could increase the track counting rate and improve counting reproducibility. However, separating elements in nontrivial images is one of the hardest tasks in image processing. Several solutions were presented for separating, counting or measuring tracks automatically; still, the precision of automatic methods is not satisfactory yet. The major challenges to automatic track counting are detecting overlapping tracks, distinguishing tracks and material defects, and identifying small tracks and defects in the background of photomicrographs. Here we study these issues using three-dimensional track geometry and an adapted two-dimensional approach given in de Siqueira (2018). This system allows us to count and measure fission tracks in apatite and muscovite samples.
G0814. Characterising Measure While Drilling data responses to changes in rock hardness Katherine L Silversides, Arman Melkumyan Measure While Drilling (MWD) data is collected during blast hole drilling and provides indirect information about the rock strength, as well as relative information on the strength changes between adjacent rock units. In the banded iron formationhosted iron ore deposits in the Hamersley Ranges of Western Australia, exploration drilling typically has a 50m spacing (0.1-2m vertical resolution). MWD data is
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collected on the closely spaced ( 5m, 0.1m vertical resolution) blast holes and can provide more detailed information. MWD measures multiple variables, including penetration rate and torque. These variables do not directly correlate to rock type and can be affected by other factors. Adjusted penetration rate (APR) adjusts the penetration rate for drilling inputs, allowing it to more accurately represent the changes in rock strength. Changes in rock type can be identified as features in the MWD signal. This deposit contains soft shale waste rock (high APR) and harder iron ore (low APR). The contact between these units can be seen in the APR. Gaussian Processes (GPs) were selected to characterise and automatically identify this contact, as they allow the change to be identified using the data (capturing the variability in the feature) rather than a hard cut-off, are less affected by noise, and provide a probabilistic output. The GP was trained using a library created from APR examples that were selected using the predictive uncertainty and the single length-scale squared exponential covariance function. This GP was used to process a test area ( 3000 holes) with three different bench heights, allowing the consistency of the method to be validated between benches. As a ground truth was not available, the results were assessed by visually comparing them to the APR and the existing deposit model. While some false positives occur due to noise, the features identified by the GP appear generally correct.
G0815. Study of Risk assessment to Linear Engineering Structures due to Thermokarst Processes on the basis of remote sensing and mathematical modeling Veronika Kapralova Thermokarst is one of geocryological processes especially sensitive to anthropogenic intervention and climatic changes. Many researches study thermokarst processes, but statistical methods are less studied, in particular we may tell it about analysis of quantitative aspects of thermokarst processes. This work aims at trying to develop and substantiate the quantitative risk estimation procedure for linear objects with the help of remote sensing and methods of mathematical morphology of landscape. In our work we use a method of mathematical morphology of a landscape - a branch of landscape science, investigating quantitative laws of landscape mosaics and methods of the mathematical analysis of these mosaics (Viktorov, 1998). After data analysis, the probabilistic mathematical dependences reflecting the most essential geometrical properties of a pattern for territories with thermokarst processes have been developed by A.S. Viktorov (1998, 2006, 2016). We performed the model approval in several test districts. We selected the districts based on morphological homogeneity and availability of remote sensing data. The obtained results analysis shows general correspondence of calculated and empirical data and allow to suggest the method to estimate damage risk for engineering structures due to hazardous exogenic geological processes. The result of the studies performed is as follows: 1. Mathematic risk estimation models for linear engineering structures damaging by hazardous exogenic geological processes have been validated based on mathematical morphology of landscape.
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G08. Machine Learning, Pattern Recognition, Data Mining, Big Data 2. Models have been experimentally tested for the territories where thermokarst processes develop. 3. The possibility of calculation of linear engineering structures damaging risk by hazardous exogenic geological processes with the help of repeated satellite images has been shown. The research is done with the support of Russian Scientific Foundation Grant # 18-17-00226
P0217. Study on data chains, big data minig and super-computer platform-based intelligent monitoring, simulation, control and early warning of urban soil pollution Yongzhang Zhou, Xiaotong Yu, Fan Xiao Intelligent monitoring, modeling, control and early warning of urban soil pollution is an important part of urban geo-environmental space-time perspective and intelligent control based on big data, meanwhile, it is the demand from the construction of Smart City. Based on the Tianhe-2 supercomputer platform affiliated to Sun Yat-sen University, the urban soil pollution database of the rapidly developing Shenzhen City was built by integrating and melting the huge amount of data obtained from soil pollution investigations and monitoring sites as well as the networks. Dynamic monitoring data chain was formed through the automatic data acquisition, update and iteration. Big data analysis of urban soil pollution was conducted in order to study the geochemical field of soil and the spatial variation and the sources of pollutants. Soil uptake rate-based human health risk assessment, as well as soil safety grading and zoning, was carried out. Urban soil pollution forecast and early warning were studied using big data chains to provide panoramic spacetime perspective and early warning services. Operational technical architecture and data models of intelligent monitoring, simulation, control, early warning of urban soil were established, as well as the software prototype for decision making system. This study offers support to the smart city construction of Shenzhen City, especially in the aspect of the construction of the big data-based urban geo-environmental perspective and intelligent control software platform.
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G09 Numerical Modelling and Numerical Simulation Ivan Kennedy
G0901. An application of the restart Ensemble Kalman filter for the identification of contaminant source in a sandbox experiment with uncertainties Zi Chen, J. Jaime Gómez-Hernández, Teng Xu, Andrea Zanini, Fausto Cupola Contaminant source identification is a key problem in groundwater pollution which has attracted much attention. Among several methods, the restart Ensemble Kalman filter has proven its capacity to identify a contaminant source in synthetic cases. In this work, the restart EnKF is utilized in a sandbox experiment to identify several parameters, including contaminant source location, injection information and releasing time together with information about the position of a vertical barrier that deflects the main contaminant path. The experiment was performed in the sandbox using sodium fluorescein as the tracer. The luminosity of fluorescein during the experiment was captured by a digital camera and then the fluorescein concentration measurements were computed, at each grid node, from calibrated luminosity-concentration curves. Beginning with a vague prior information about the unknown parameters defining the concentration source and the barrier position and size, the restart EnKF is employed to identify them by assimilating the fluorescein concentration measurements. The influence of data and model uncertainty on the performance of the restart EnKF is also discussed. The result shows that an underestimation of observation uncertainty will lead to a bad estimation, while an overestimation will lead to inaccuracies. Model uncertainty has also an impact and needs to be considered. In general, the restart EnKF is capable of recovering the contaminant source and barrier information while taking the observation and model uncertainty into account.
G0902. Generating variable shapes of salt geobodies from seismic images Nicolas Clausolles, Pauline Collon, Guillaume Caumon Modeling 3D salt geobodies from subsurface data usually requires lots of time and expertise. The difficulties encountered stem from the assumptions underlying geomodeling techniques. These techniques are designed for almost vertically singlevalued surfaces, and generally minimize the surface curvature and the layer width variations. These assumptions are not verified for salt envelopes, which are thus of-
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G09. Numerical Modelling and Numerical Simulation ten manually modeled with a deterministic expert-driven approach. We present in this paper an implicit methodology to generate automatically several possible models of salt top surfaces with varying geometries and topologies. As seismic imaging of salt is prone to velocity uncertainty and Fresnel zone effects, we take as input a seismic image that we segment into three regions: salt, sediments and uncertain, using seismic attributes. The uncertain region is assumed to contain the salt boundary and all the further computations will focus within this zone. We generate a monotonic scalar field ranging from zero at the contact with salt to one at the contact with sediments. This scalar field can be seen as the cumulated probability for any point of being outside the salt boundary, i.e., to be sediments. We then generate a random scalar field, also bounded between zero and one, that we use to threshold the first field. The salt boundary is implicitly defined by the zero isovalue of the difference of the two fields, and can be further extracted using marching cubes. We illustrate the method on a 3D synthetic data set and discuss the geometrical and topological implications of the choice of the random field parameters.
G0903. Geostatistical Reservoir Characterization to Predict Best Litho-fluids discriminators for Lumshiwal Sandstone: A Rock Physics Based Study Nisar Ahmed, Mubasher Ahmad, Perveiz Khalid Reservoir characterization needs to transmute the seismically estimated parameters such seismic velocities, impedances, elastic moduli and other rock physics parameters into lith-fluid indicators. A large number of rock physics equations provide the relation between seismic velocities and reservoir rock properties. We have applied the rock physics modeling that aims to direct prediction of best litho-fluid discriminators for Lumshiwal sandstone of Cretaceous age; a widely distributed reservoir rock in Kohat sub-basin and Punjab platform, Pakistan. The workflow is based on numerical simulation of rock physical model so that different seismic and elastic parameters are calculated as a function of pore fluids (gas/water) and then bivariate probability density functions (pdfs) of mutually cross-plotted attributes are generated and plotted. To better assess the best litho-fluids indicator for reservoir rock 2D joint pdfs contours of various attributes such as acoustic and extended elastic impedances, P to S wave velocities and their ratio, Lamé’s constant and their product with density, pore space modulus, fluid stack (ratios of Lamé’s parameters), Poisson’s ratio are cross-plotted. Fluid indicator coefficients describe the sensitivity strength to pore fluids for each attributes ranging values 0.16 to 0.78 is also calculated. Fluid stack (λ/µ), normalized elastic impedance and Russell’s fluid term (�*f) are best attributes to discriminate the gas-saturated facies from shale and brine saturated sand.
G0904. Mathematical model of erosion and deposition in deformable porous media Eduard Khramchenkov, Maxim Khramchenkov, Denis Demidov We present mathematical model of erosion and deposition processes in an exposed reservoir rock. The model is based on the theory of flow in deformable porous media with mass-variable skeleton. It is shown how precipitation of particles which were eroded from solid phase by fluid flow near the well (suffosion) causes clog-
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ging of porous space at some distance from the injecting well where flow velocity is lower. Numerical solution of the model in 3-D case is developed. High performance AMGCL solver library was used for calculations on a GPU. Double stage preconditioners were developed to enhance performance of solution of coupled systems. This type of preconditioners gives opportunity to effectively solve problems on which classical multigrid method and other single stage precondtitioners have bad to none convergence. Calculations were performed on Nvidia Tesla K20 GPU. CUDA backend gave the best performance on this problem. Combination of incomplete LU relaxation and L-GMRES iterative solver was found to be the most effective for solution of the linear system. Porosity was calculated with backward Euler method. The 3d transport equation for concentration was solved using the corrected Friedrichs (CF) scheme, since no large gradients or shock waves were present. Obtained results show good correlation with known analytical solutions and experimental data.
G0905. Natural Gas Demand under Multi-Factor Orthogonal Decomposition Method Wei Yan, Yuwen Chang Natural gas demand is affected by some factors, which do not necessarily show linear superposition. Moreover, there is a correlation between various factors. Therefore, the key issue is that how the nonlinear synthetisation affect natural gas demand. A class of optimal synthesis method is formulated. It constructs an orthogonal coordinate system and describes the multi-factor synthetisation. The rate of change can be defined to replace the “the uniform form of units”. By using a new measurement method and constructing an orthogonal coordinate system, we can draw on each impact of chief factor on the object decomposition and synthesis. Then we can draw the equilibrium equation in the direction of each orthogonal axis. Moreover, supplementary equations also be obtained. and obtain the trends of the object finally. It can calculate the world and some regions’ natural gas demand trend. The growth of natural gas demand in the next 20 years is gratifying. By 2030, the annual average growth rate can reach more than 2%, and the demand is about 4791bcm/year. Asia Pacific affected by the economic rapid growth of whole economic situation, can be maintain the demand at the same level as North America around 2020. To 2030, the Asia Pacific can exceed the level of 1200bcm/year. In North America, the demand of natural gas is large volume. the basic demand for natural gas, to 2025-2030, maintain the level of 928-948bcm/year stability. Europe is affected by economic development and low-carbon development. The development of nuclear power is limited. The import volume of natural gas will increase significantly. The demand will reach 730bcm/year by 2030. It is rich of natural gas resources in Central Asia and Russia. Russia has been rejuvenating the economy in recent years. By 2030, the natural gas demand of Central Asia and Russia will be up to 647bcm/year.
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G09. Numerical Modelling and Numerical Simulation
G0906. Numerical approach for faster and precise pressure and overpressure analysis in petroleum system modeling Renaud Traby, Mathieu Ducros, Isabelle Faille, Françoise Willien Petroleum system modeling provides highly valuable information for oil and gas companies decision making during their exploration phases. However, simulations with these powerful tools can require really long computation time depending on the complexity of the geological history and physical processes involved. Moreover, it is usually difficult to deconvolute the effects of the many coupled phenomena responsible for the results. These technical limitations reduce the time that is effectively dedicated to the project risk appraisal itself. This consideration is particularly true for pore pressure studies, for which simulations are even more time consuming in overpressured basins. Furthermore, it remains difficult to discriminate the possible sources of abnormal pressure due to the complex imbrication of geo-physical processes. We propose a numerical approach, allowing important time reduction for pressure calculation while ensuring high quality results. We first use an empirical law to evaluate the overpressure that would be developed at the end of a numerical event if the model followed the backward history. If the estimated overpressure of all the cells of the model is inferior to a criteria, the model is simply defined as hydrostatic. If not, a complete calculation of the pressure is performed. For that purpose, a new numerical scheme has been developed, decoupling the lithostatic potential and the overpressure. Those substantial time gains are completed with a deconvolution of the main effects responsible for the abnormal calculated pressures leading to powerful interpretations on petroleum systems and play analysis.
G0907. Physical simulations on geological models using unstructured grids Margaux Raguenel, François Bonneau, Antoine Mazuyer, Thomas Driesner Geological models and simulations of physical processes are becoming an asset in decision making for geosciences application. In geothermal applications for example, it helps understanding the processes occurring in the reservoir to target potential hot spots [Ingebritsen et al., 2010]. However, it is often needed to deal with several tools and different file format to obtain the desired results. Enabling the conjoint use of different software represents a major challenge in numerical simulations. Therefore, it can be a drawback to develop further progress in the comprehension of subsurface geological heterogeneities and their impact on coupled physical processes. In this work, we propose an interfacing tool allowing the direct communication between two libraries: (1) RINGMesh [Pellerin et al., 2017], which handles 3D unstructured meshes of complex geomodels and (2) CSMP++ [Matthaï et al., 2007], which performs physical simulations using a finite volume-finite element resolution scheme. The first step to establish this communication is to extract and transfer the geometrical, topological and geological information from RINGMesh to CSMP++. This representation is, in a second step, used to set the physical model by describing the equations and boundary conditions. Simple geomechanical simulations have been performed to validate the approach and demonstrate the ability to run physical processes on more or less complex geomodels. Finally, a flow simulation has been done to show the capacity of the tool to handle various physical processes and open the path for further complex coupled geothermal applications.
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G0908. Proxy model for hydraulic fracture propagation and seismic wave propagation processes in a fractured reservoir Manik Singh, Sanjay Srinivasan Characterization of discrete fracture networks (DFN) is necessary for characterizing an unconventional reservoir as they control the flow from hydraulically fractured well. The interpretation of micro-seismic data provides information about the discrete fracture network (DFN) in the vicinity of a well. While, interpretation of micro-seismic data is currently handled in a deterministic manner, the inference of fracture related information from such data is likely to be non-unique. To address the non-uniqueness our workflow involves applying a forward model which produces synthetic seismogram generated while hydraulically fracturing in a naturally fractured reservoir. The available full physics models are computationally expensive. Hence, a proxy model is developed which is computationally inexpensive so it can be applied on a large ensemble of models in Bayesian model selection framework. Generating seismic waves from a fracturing job involves many intermediate processes such as diffraction, reflection etc. As analytical solutions for most of these processes exist, a coupled analytical model is proposed. Firstly, we have a hydraulic fracture propagation model, then it is coupled to another model which dictates the outcome of an interaction between a hydraulic fracture and a natural fracture. This interaction results in slip events at the natural fracture and might also result in initiation of more hydraulic fractures from the natural fracture. With the knowledge of location of slip event a Green’s function solution is generated to model the propagation of the seismic wave. Using the results from slip event and Green’s function a synthetic seismogram is generated. Finally, seismograms are adjusted for the changes in amplitudes as signal passes through several natural fractures. Proxy model has more assumptions as compared to a full physics model. Validation of individual elements of the coupled proxy model to experiments are shown. Also, comparison of developed proxy model is done with more computationally expensive models.
G0909. Regional gravity field improvement and its application to geophysical modelling in Antarctica Theresa Schaller, Mirko Scheinert, Roland Pail, Petro Abrykosov, Philipp Zingerle The determination of the exterior gravity field of the Earth is one of the main tasks of geodesy. For this, different datasets have to be combined. Dedicated satellite missions provide global data of lower resolution. To get the desired high resolution (down to 10 km half-wavelength) terrestrial gravity data have to be incorporated. Within a current project we are aiming to come up with an improved regional gravity field solution for Antarctica, which will be applied to further geophysical investigations. With regard to terrestrial measurements Antarctica is a special case due to its vast extension and harsh environment. Although a considerable number of gravity data have been acquired in the recent years, large data gaps still remain. Furthermore, these data have different resolution and accuracy. Also, there might exist biases and further systematic errors. A principal problem arises utilizing satellite data. Due to the satellite’s orbit inclination of less than 90°, a polar data gap occurs. For GOCE, that has the highest resolution of all satellite gravity missions, this polar data gap has a diameter of about 1400 km.
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G09. Numerical Modelling and Numerical Simulation In this project we aim at addressing these difficulties and develop novel approaches to optimally combine the different datasets. We will investigate techniques to infer an improved global gravity field model together with a consistent regional solution for Antarctica. We will discuss the applied methods such as spherical harmonic expansion, tailoring and weighted adjustment. The new gravity field solution will then be used in geophysical modeling, especially for subglacial topography and the Moho discontinuity. Here, e.g. Parker-Oldenburg inversion will be used, while incorporating constraining data such as ice-penetrating radar, grounding line locations and seismic studies. In our presentation we will highlight the challenges of this study, discuss the applied analysis techniques and present first results.
G0910. Reservoir Characterization of Cretaceous Sand to Predict the Pore Fluid heterogeneities by Applying AVO Attributes: A Case Study from Indus Basin, Pakistan Mubasher Ahmad, Nisar Ahmed, Perveiz Khalid Amplitude versus offset analysis (AVO) and seismic petrophysics are principal techniques used for reservoir characterization and to predict the effect of fluid heterogeneities in clastic sediments on seismic signature. In this study the mathematical algorithm of Gassmann fluid substitution is used to estimate the seismic velocities and densities as a function of pore fluids and then P and S wave reflection amplitudes are computed at different fluids saturation levels by using Zoeppritz set of equations. Conventional amplitude based attributes including P converted P and S and S converted P and S reflection amplitudes and some newly generated attributes are applied on two different cases of the Lumshiwal Sandstone to analyze the best litho-fluid indicators. The new AVO attributes are developed by the difference between fully wet reservoir rock and reflection amplitude at unknown gas saturation. This study is carried out in the Indus Basin Pakistan: the only hydrocarbons producing sedimentary basin. This quantitative analysis discloses that the reservoir rock exhibits different amplitude behavior in both cases and newly generated P converted P and S converted S reflection amplitudes (ΔRPP and ΔRSS) are good indicators to assess gas zones as compared to the conventional P converted S and S converted P attributes (RPS and RSP). The intercept gradient crossplots are prepared to define the AVO class of gas sands. Same reservoir rock shows dissimilar class and falls in quadrant II and IV in intercept gradient plots. The mathematical script of this simulation is written in MATLAB®.
G0911. Testing the hypothesis that variations in atmospheric water vapour are the main cause of fluctuations in global temperatures Ivan Kennedy, Migdat Hodzic Abstract: The water vapour content of the atmosphere is the major cause of the natural greenhouse effect, contributing more than two-thirds or 23 C of the 33 C warming; this corresponds to 108 out of 155 W/m2 of greenhouse radiative forcing, a forcing sensitivity of about 0.213 C/W m-2. In contrast to the effects from the globally well-mixed greenhouse gases such as CO2, spatial and temporal variations in soil moisture and relative humidity of the atmosphere are obviously the main factors controlling the regional outgoing longwave radiation (OLR). This 66
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is well illustrated in the 4-6 year El Nino cycles, resulting in a global mean temperature increase of up to 1 C compared to those of La Ninas. This results from accelerated evaporation from a warmer Pacific Ocean surface, convectively elevating the maritime atmosphere and reducing the global outgoing longwave radiation (OLR) to space by about 4 W/m2 globally from an increase in global water vapour of about 4%. In climate models, water is assigned a secondary though important amplifying role, solely as a positive feedback by its increase in an atmosphere previously heated by other GHGs. However, this conclusion ignores the increasing use of water to grow crops for the human population, particularly in regions with dryer soil and lower humidity. Our numerical analyses suggest that extra steady state atmospheric water vapour from irrigation could exceed 1% by 2050 compared to 1900, reducing the global OLR by more than 1.0 W/m2, exceeding the warming effect of CO2. Fortunately, this hypothesis can be tested, using, for example, the satellite data on OLR acquired since 1980, relating this to local trends of increasing irrigation or major floods in arid regions.
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G10 Spatial Statistics K. Gerald van den Boogaart
G1001. A regression model for crystallographic orientations subject to phase transformation Richard Arnold, Florian Bachmann, Peter E. Jupp, Helmut Schaeben The variant problem of texture analysis searches for an orientation relationship between crystallographic orientations of crystallites within a polycrystalline material before and after it was subjected to a phase transformation. A crystallographic orientation is a left coset UK of the special orthogonal group SO(3) with respect to some symmetry subgroup K � SO(3); the set of all cosets is the quotient space SO(3)/K. The orientation relationship is provided by a rotation A inducing a relation A˜ on the Cartesian product SO(3)/K1 ×SO(3)/K2 where the former refers to the pre-transformation and the latter to post-transformation phase. This relation assigns to any crystallographic orientation of SO(3)/K1 a subset of SO(3)/K2 comprising the variants. For given phase transformations theoretical models of orientation relationships exist based on crystallography and solid state physics. The orientation relationship may also be empirically modeled from given pairs of spatially referenced orientation measurements (Ui, Vi), i = 1,...,n, as provided by electron back scatter diffraction (EBSD), for instance, where Ui refers to the pre-deformation and Vi to the post-deformation phase. An orientation relationship is not a regression from SO(3)/K1 to SO(3)/K2 but the observable part of an unobservable regression from SO(3) to SO(3)/K2. Our regression model is given in terms of a novel family of exponential distributions of cosets of SO(3) recently introduced by the authors. Applications of analysis of variants include comparison of theoretical and empirically fitted models, and modeling initial orientations and their statistical and spatial distribution allowing for fabric analysis of the pre-deformation phase.
G1002. Geochemical Element Combination Anomalies Extraction Based On Spatial Neighborhood Local Correlation Coefficients Zhaoying Yang, Kang Wu, Jin Qin, Wang Yao, Ying Zhan, Xianchuan Yu Geochemical element combination anomalies are often reflected by the correlation between the elements. The correlation coefficient is a measure of the correlation. However, the traditional methods for calculating the correlation coefficient only reflect the global relevance of the elemental combination. In fact, elemental anomalies often exist in certain geological backgrounds, such as fault zones and rock mass, which lead to some element anomaly regions being submerged in the global
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G10. Spatial Statistics area and hard to be efficiently extracted. To solve this problem, this paper proposed a local correlation coefficient method based on spatial neighborhood, which reflected the correlation of elemental combinations through the global distribution of local correlation coefficients. In this method, the element values of the sampling region is mapped into the matrix space. The 3x3 neighborhood is used as the calculation unit, and the correlation coefficients of different element matrices in each corresponding unit are calculated as the local correlation coefficient of the center of the unit. Scan the global area and calculate the correlation coefficient matrix, then display the calculation results in thermodynamic diagram. For the experiments we select two constituencies in Henan Xiaoshan district (China) and extract the parts where correlations of elemental combination are strong. The results show that the strong correlations between elements are mainly located in the fault zone and the rock mass. It is proved that the local correlation coefficient is effective in extracting anomalous areas of geochemical element combinations, which is conducive to exploring the relationship between the geological background and elemental composition anomalies.
G1003. Limitations of spectral analysis of sedimentary proxy records, tested on simulated time series with timescale error and variable temporal resolution István Gábor Hatvani, Péter Tanos, Zoltán Kern Sedimentary proxy records (e.g. speleothems, ocean sediments) are vital climate archives of the past, well distributed globally. Thus, are valuable for intra- and intercontinental assessment of past global changes. One of the key tools in exploring the climate signal is spectral analysis. However, the timescale error and variable temporal resolution of the proxy may bias the spectral characteristics. Thus, the aim of the research was to stochastically model the spectral bias caused by timescale error on simulated time series resembling the characteristics of real-life sedimentary proxy records. To achieve the aims, an annually sampled gap- and timescale error-free time series was used. It was resampled in a controlled way to simulate different time resolutions, and timescale error was added to it, to mimic a sedimentary proxy record with chronological uncertainty. To do so, the actual historical date of the record was the expected value and the standard deviation the arbitrary chosen uncertainty of the timescale. An ensemble of potential timescales was retrieved and their spectral characteristics were explored. These steps were taken for different resolutions as well. Results suggest that for spectral analyses the resolution is the bottleneck of the analysis, while for coherency tests both resolution and the timescale error are the ones determining the methodological boundaries.
G1004. Linear compositional trend for the frequency of ocean wave events. A Bayesian approach. Maribel Ortego, Jesus Corral-López, Juan José Egozcue, Jan Graffelman The yearly frequency of classes of ocean wave storms in an off shore site is assumed to be a multinomial sample. The multinomial probabilities are assumed 70
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to be a time evolving composition, possibly due to climatic trends. Compositional techniques allow to represent compositions in isometric log-ratio-coordinates. These coordinates are modelled as multivariate normal distributed. In order to simplify the model, the asymptotic distribution of the coordinates coming from multinomial counts is used. In this approach the covariance matrix of coordinates is a function of the mean. A linear temporal trend is introduced for the mean coordinates. This model is estimated using Bayesian techniques. The fit of the model and the linear trend involving extreme events are assessed.
G1005. Manage of High Pressure Salt Water Invasion Between Salt Layers with High Density OBM in Deep Well Jianhua Wang, Wei Zhang, Haijun Yan, Xianguang Xu, Man Zheng Kuqa’s piedmont structure located in Tarim Basin of China is charactered by HT, HP ultra thick salt-gypsum bed and high pressure salt aquifer, has been identified as one of the most complicated drilling regions in the world. The occurrence rate of high pressure salt water invasion during the drilling progress is high up to 56% in Keshen Block. Normally there are two high pressure salt water invasion management methods, one is increase the drilling fluid density until its higher than the formation pressure, but the easier mud loss will occur. The other is discharge the salt water in batches to reduce the high pressure salt aquifer pressure to normal and then back to drilling, which request the higher anti salt water invasion capacity limit of OBM. A new emulsifier used in oil based mud was developed to enhance the emulsifying efficiency through the increase the number of hydrophilic group on single emulsifier molecular structure, which enhanced the anti salt water invasion capacity limit dramatically. The experiment results indicated that the anti salt water invasion capacity of OBM is higher than 60%. Well KS1101 encountered lost formation and high pressure salt aquifer In the same section, the safe density window is almost zero, the method of discharging salt water in batches was used to reduce the salt aquifer pressure to ensure the drilling safety. Totally 1129.98m3 salt water was discharged by 64 times and severe loss caused by well killing was avoided. The favorable rheological property of was maintained from the beginning to the end even the salt water capability is more than 45%, and there was no downhole complex occurred such as pipe blocking caused by salt crystallization, wellbore instability, pipe stick and so on. This OBM system has been wildly used in Keshen Block.
G1006. Modelling undiscovered oil resources: A stochastic geometry approach Erik Anderson This demonstration of a modelling system explores an estimated 3.5 billion barrels of undiscovered oil posited to exist in a study area of 22,756 square Km in Arctic Alaska. The approach is characteristic of certain theorems of Stochastic geometry and Geometric probability developed by mathematicians during the latter half of the 20th century. The undiscovered resources in a complex suite of plays is transformed from play estimates into a mass of undiscovered accumulations. The resulting mass of undiscovered accumulations is analyzed to produce a series of maps and graphs revealing a variety of strategic insights. Among these a composite map 71
G10. Spatial Statistics showing contours for both the expected value (EV) for random discovery volumes and chance of success associated with a hypothetical well. A distinct regional trend or “statistical play” of elevated favorability gives some understanding of the significance of the new information this approach can yield. A separate analysis explores the effect of spatial efficiency of mineral land holdings on the expected finding cost of the reserves. Thus, these issues may now be more clearly quantified and addressed by the entities involved. As the model is explored further the temporally dynamic nature of these resources is revealed as the recent drilling activity in this region interacts with the modelled undiscovered accumulations. Compelling validation of this model is evident as the results from actual exploration wells correlate with the model generated EV of random discovery volumes. Aside from applications in a few fields of science these techniques have received little attention, most surprisingly, in the geosciences involving undiscovered hydrocarbon resources.
G1007. Universal law for waiting internal time in seismicity and its implication to complex network of earthquakes Norikazu Suzuki The concept of complex earthquake networks has been attracting much attention. There are two different methods of constructing networks from seismic data: one is of Abe and Suzuki (AS) [1] and the other is of Baiesi and Paczuski (BP) [2]. The point is that the BP method is based on the work of in [3], which claims a unified scaling law for waiting times for earthquakes, i.e., given an event in a spatial cell, after how much conventional time the earthquake occurs in the cell next. Soon after the work in [3], it has been found [4] that actually such a ”unified” scaling law depends on data sets and is therefore not universal. In this Talk, we show that there certainly exists a unified scaling law if internal time termed ”event time”, which labels events, is employed. In contrast to the conventional waiting time, the waiting event time obeys power-law statistics [5]. This implies the existence of temporal long-range correlations for the event time with no sharp decay of the crossover type claimed in [3]. The law turns out to be universal since it takes the same form for seismicities in California, Japan and Iran. In particular, the parameters contained in statistics take the common values in all these geographical regions. This fact explains why the AS networks based on event time possess many properties universal for different data sets, whereas the BP ones do not. [1] S. Abe and N. Suzuki, Europhys. Lett., 65 (2004) 581; Nonlin. Processes Geophys., 13 (2006) 145 [2] M. Baiesi and M. Paczuski, Phys. Rev. E 69, (2004) 066106 [3] P. Bak et al., Phys. Rev. Lett., 88 (2002) 178501 [4] V. Carbone et al., Europhys. Lett., 71 (2005) 1036 [5] S. Abe and N. Suzuki, Eur. Phys. J. B 44 (2005) 115
G1008. Bayesian Prediction of Spatial Data with Non-Ignorable Missingness Using INLA and SPDE Mohsen Mohammadzadeh, Samira Zahmatkesh Nowadays researchers are dealing with spatially dependent data in various sciences such as climatology and ecology, in which there is often a notable amount of missing values. Factors like atmospheric conditions, temperature variations, and special regional coverage could be the cause of this missingness. As we know in 72
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spatial data, individuals in the vicinity may have some characteristics which could directly effect on estimations and predictions. This means that characteristics of nearby missing values may contain potentially useful information so that ignoring them or handling inappropriately may lead biased and inefficient inferences. In addition, in this case it is expected some latent spatial random fields be the cause of missingness so the missing process would be non-ignorable and a valid inference requires modeling missing data process and incorporating it into the Inferences. In this paper, inferences are based on a joint model of both the measurement and missing data process so that a common Gaussian random field has been considered for modeling both of them and this random field will be used in order to describe nature of the association between these two models. A computationally effective approach is given by stochastic partial differential equation which consists in performing computation using a GRF thus allowing us to adopt the integrated nested Laplace approximation approach. Simulation studies have been performed for evaluation of the proposed model and results has been reported.
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P0102. A novel approach for characterizing the spatial heterogeneity of coastal morphology - case study at southern Baltic Sea Junjie Deng, Jiaxue Wu, Wenyan Zhang, Joanna Dudzinska-Nowak, Jan Harff There needs a method to identify a coastal tract with almost uniform alongshore morphological characteristics, which is assumed at many coastal profile modelling approaches. This paper applies relaxation distance to characterize the statisical alongshore length of the coastal tract, on a basis of the equilibrium concept. The relaxation distance describes the transitional distance from one equilibrium coastal morphology to another. Here, we applies this environmental variable to analyze 225 cross-shore submarine profiles spaced every 500 m at three distinct southern Baltic coastal sections with varying wave and current conditions. A semi-variogram approach is applied to quantify the relaxation distances. The results indicate that the relaxation distances successfully reflect the alongshore morphological similarity and variability within and among these sections. In general, relaxation distance decreases with increasing incoming wave energy and enlarged amplitudes of crossshore morphological perturbation. Large amplitudes of cross-shore perturbation, such as longshore bars and channels, have significant impact on the total sediment budget in the coastal sections. The presented approach is also potentially useful for studying the morphological structure of other earth surface landforms.
P0103. A point exchange non-dominated sorting algorithm for Pareto optimal space-filling designs Kago Kebotsamang Space-filling designs offer a great alternative sampling approach for environmental problems when there is limited information about the process under study. These designs are based on geometric criteria and largely avoid misspecification of covariance structure for the spatial process. Efficient space-filling designs are constructed by optimizing a combination of different criteria, which essentially makes their construction a multi-objective optimization problem. A Pareto optimality approach to multi-objective optimization is gaining popularity in optimal designs because of its ability to generate a set of designs that satisfy a predetermined optimum definition as opposed to a single global solution. However, few algorithms have been developed to construct Pareto optimal designs and these are inefficient for medium to large design problems. In this paper, we propose a new algorithm that uses an elitist principle and diversity mechanism for constructing Pareto optimal designs. The 75
P01. Poster Session A elitist principle ensures that superior designs are carried over to the next iterations, making the algorithm more effective and efficient.
P0104. Agent based system for the rangeland management ”application in Djelfa province Algeria” Zerguine Abderrahman, Belhadj Aissa Mostefa The Algerian steppe is a vast intermediate zone between the Sahara and Tel. It is considered a phenomenal cradle of a rich socio-economic life based on Agropastoral activity. The steppe area is subject to substantial degradation under the influence of soil and climatic unfavorable conditions to maintain the site ecological equilibrium (prolonged drought, erratic rainfall, poor soil ... etc.). Note that this degradation is also accentuated by the inadequate human action on the one hand (clearing, cuts, and fires) and the action of the herd on the other hand (overgrazing, trampling). Despite numerous studies on this region, which offer significant mass data, synthesis and simulation of the steppe area dynamics is still lacking. It is in this vision, our research project integrates. The main objective is the development of a management tool for the wide steppe taking Djelfa region as the study case. This is possible through the use of multi agent system that is an intelligent tool to predict future states. The simulation and prediction are helping us to make a powerful decision for a sustainable management of steppe and overcome existing conflicts between different actors and their economic and environmental interests.
P0105. Application of Geostatistical Techniques for the Determining of Anomalous Zones of Copper Ore Deposit Barbara Namysłowska-Wilczyńska A paper presents the results of investigations into the spatial variation of the basic geological parameters of strategic mineral resources, i.e. the copper ore deposits occurring in the Lubin-Sieroszowice region (SW part of Poland). The studies were based on data obtained by sampling the deposit with groove samples distributed mostly uniformly (at a spacing of 15÷20 m) over the area of Polkowice mine. The studies concerned the Cu grade, thickness and quantity of the (recoverable) deposit concentrated in Weissliegend sandstones, Zechstein copperbearing shales and calcareous-dolomitic formations. The bundled indicator kriging and conditional turning bands simulation have been used for the determining of anomalous zones of copper ore deposit within the mining block P-1. The indicator semivariograms for the assumed cutoff values of the studied parameters: Cu grade > 0.7 %, 2.5 %, 3.5%; thickness > 2 m, 3.0 m, 3.5 m; quantity < 35 kg/m2, > 35 kg/m2, 50 kg/m2 were calculated. Probabilities P in the block centres of the elementary grid, covering block P-1, were estimated by means of the bundled indicator kriging, taking into account the determined parameter values of the semivariograms models. As a result of the performed indicator estimations, has been obtained the picture of the probabilities P distributions of the exceeding of the assumed cutoffs. In a successive stage of the spatial analyses a conditional turning bands simulation was applied. The turning bands simulation was performed, taking into
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calculations of the theoretical models fitted to the Gaussian semivariograms of the deposit parameters. Raster maps of the distributions of simulated values Zs and probabilities P of exceeding of the assumed thresholds, similarly as for bundled indicator kriging, were elaborated. A comparison of the obtained results concerning both used geostatistical techniques makes possible a better choice, indicating researchers more useful method for estimating of mineral resources parameters.
P0106. Denoise for Soil Geochemical Data Based on Sparse Representation Wei Youhua, Lin Wu, Xiangquan Zhou, Huan Liu Soil geochemical data can reflect the changes of chemical elements in the soil. By studying the rules of dispersion and concentration, related researchers can delineate anomaly areas and provide effective gist for the field investigation and prospecting. As soil geochemical data generally contains noise, Geological researchers find it hard to delineate precisely anomalous regions with the original data. Therefore, it is essential to reduce the impact of noise. However, the traditional denoising algorithms ignore the peculiar data characteristics of soil geochemical data and denoise in the way of suppressing saltation values, which can destroy the data structure of the original data, even destroy some useful information during denoising and should have negative impacts on subsequent delineation of anomalies. Researches show that geochemical data are sparsity, the ideal soil geochemical data can be represented as a linear combination of few atoms from the redundant dictionary, and noise is the approximation residuals from the actual data. Therefore, denoising algorithm based on sparse representation cannot suppress saltation values. For this purpose, on the basis of preserving the data structure of soil geochemical data, we denoise the data via using blocks and block overlap and finally use the Structural Similarity Index (SSIM) as the measurement of data fusion, combined with the denoising principle of sparse representation. Analyzing the specific application results, it shows finally that the sparse representation can effectively denoise the soil geochemical data.
P0107. Geological modeling of porous carbonate reservoir based on seismic and rock type Mingchuan Wang, Taizhong Duan Porous carbonate reservoir is highly heterogeneous, the property, especially the porosity – permeability correlation is poorly controlled by sedimentary facies or lithofacies, it is difficult to establish the property model which can reflect the reservoir dynamic characteristics. In order to solve the geological modeling problem of porous carbonate reservoir, instead of traditional facies controlled geological modeling method, a seismic and rock type based geological modeling method is put forward. By fitting the pore throat radius, porosity and permeability under different mercury injection saturation of the core experimental data, a modified Winland R35 rock typing method is presented. 5 rock types are divided according to the pore throat radius, and the correlation equation between porosity and permeability of each rock type is obtained by fitting. Then the pore throat radius model and porosity model are built by Sequential Gauss Simulation method (SGS) constrained by seismic acoustic impedance respectively, and rock type model is truncated from the pore throat radius model by the rock type classification boundary. Permeability model is obtained according to the porosity – permeability correlation equation of
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P01. Poster Session A each rock type. Compared with rock type model built by Sequential Indicator Simulation method (SIS), the distribution of rock type in model built by seismic and rock type based method is more consistent with the geological understanding of geologists. Property of new well is consistent with the measured values. The method proposed in this paper improves the prediction accuracy of rock type, porosity and permeability, and provides a practical procedure for geological modeling of complex porous carbonate reservoir.
P0108. Heaps of Information – Exploratory Data Analysis of Geophysical and Borehole Data for the Investigation of Tailings at an Abandoned Mining Site using “R” and a 3D-Geodatabase. Heinz Reitner, Christian Benold, Adrian Flores-Orozco, Jakob Gallistl, Alexander Römer, Albert Schedl The authors investigated tailing heaps at an abandoned mining site by means of geo-electrical profiles and geochemical analyses of borehole samples to study the extent, structure, material properties and metal content of the heaps. Datasets of electrical resistivity, induced polarization and geochemical content were 3Dgeoreferenced for import into a 3D-geodatabase of a Geographic Information System (GIS). Using the 3D-geodatabase, a simultaneous display of the datasets on-screen enabled quality checks of location and values of the measurements as well as the detection of trends and structures in the data. Additionally, the free software for statistical computing “R” was used to retrieve and process data from the 3Dgeodatabase. Exploratory data analysis (EDA) tools were applied to investigate and interpret the datasets. The analysis of electrical imaging results and geochemistry using GIS and EDA-tools is a step forward an improved understanding of the IP method. The poster will show results of the conjoint use of the 3D-geodatabase and the “R” software.
P0109. Identification of the key domains in LGOM copper deposit Krzysztof Holodnik, Wojciech Kaczmarek Reserves estimation of the geological deposit needs to be completed with an uncertainty assessment to allow the risk appraisal. There are considered sources of the uncertainty: observations errors, chosen approach for modelling (model inadequacy), model unknown parameters and computational errors. Uncertainty increases in subsequent stages of the modelling workflow which usually starts with structural interpretation of the orebody. In Poland three KGHM copper mines operate in the area of LGOM polymetallic deposit. Mineralization of this deposit covers different type of the process sedimentary, intrusive, weathering. Structural model reflecting the main lithology complexes (dolomites, sandstones and shales) is not sufficient for reserves estimation in this case. Structural interpretation based on detailed lithology improves the estimation quality (hard boundaries and soft boundaries were used). The next correction of the uncertainty assessment was obtained when different mineralized profiles were considered. Identification of the estimation domains which uses interpretation of Zechstein sea bottom as pick, inclined or valley areas was applied.
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P0110. Impact of the compositional nature of data on reserve evaluation in a coal deposit, Iran Hossein Molayemat, Farhad Mohammad Torab, Vera Pawlowsky-Glahn, Amin Hossein Morshedy, Juan José Egozcue Coal proximate analysis is the basis of coal reserve evaluations and is a form of compositional data. Results of direct geostatistical modeling of compositional data are exposed to inconsistency and non-optimality. In this study, we compare the compositional and non-compositional approaches to assess the problems caused by neglecting the nature of data. We combine isometric log-ratio coordinates with sequential Gaussian co-simulation and cokriging and compare the results with conventional geostatistical modelling of field data from the Parvadeh IV coal deposit in Central Iran. In order to attain a comprehensive comparison of the two approaches, we use several criteria. The sum of values for grid cells over the whole deposit revealed that the non-compositional approach fails to add up to 100% for almost all cells; R-squared and RMSE of ash and carbon, are 0.96 and 0.97, respectively 1.36 and 1.46, for the compositional approach and 0.75 and 0.80, respectively 2.77 and 2.84, for the non-compositional approach. In addition to more accurate estimations by the compositional approach, the superiority of this approach is extremely distinctive in high and low grades, namely, third and first quantiles. This is critical for the correct classification of the coal. Comparison of Aitchison distances and correct classification rate (CCR) of the two approaches illustrate that the compositional approach is notably more accurate and reliable. Moments of total tonnage are computed based on stochastic simulation. Cut-off based curves showed that the results of the non-compositional approach can lead the analyst to misinterpretation in terms of underrating the deposit by considering 3 to 5 million tons of coal as waste. It is concluded that neglecting the compositional nature of data will result in deviated outputs, unrealistic models, unreliable evaluations and, finally, lead to financial losses.
P0112. Partial Grade method to improve estimation of multi-unit deposits with soft boundaries: application to an iron mine deposit Sara Kasmaeeyazdi, Giuseppe Raspa, Chantal de Fouquet, Stefano Bonduà, Francesco Tinti, Roberto Bruno In multi-unit deposits, the risk of facing uncertainties in estimating geological domains is generally high. The uncertainties increase when there is no exact boundary among geological domains, which is called “soft boundaries”. The geostatistical method of Partial Grade (PG) is an estimation technique developed for multi-unit deposits in the presence of “soft boundaries” when the spatial variability of the grades varies between different geological formations. A partial grade is the product of the indicators of the geological domains with the ore grade, so the partial grade cokriging of different geological formations can be performed in the case of multi-unit deposits. However, the partial grade cokriging is relevant only in the presence of a “border effect”, evidencing the evolution rate of average grade when moving from one geological domain to another. Additional geostatistical tools with respect to standard geostatistics, such as variogram ratio and preferential relationship schemes allows to identify the grade relation among different geological domains. In this work, we present an application of partial grade cokriging to an iron multi-unit deposit with soft boundaries located in Iran. Up to the knowledge of authors, although the PG method has been applied to several case studies in 79
P01. Poster Session A literature, this application is the first case study where the border effect is present. The three main geological domains are defined: Poor mineralization: (low grade of iron, 20 < Fe% < 45), Rich mineralization: (high grade of iron, Fe%� 45) and Metasomatite mineralization (strongly altered rock, Fe% < 45). The partial grade cokriging results have shown to reduce uncertainties in grade estimation of geological domains with respect to the classical geostatistical estimation methods (Ordinary Kriging and Co-Kriging). Results were validated with the blast hole data used as reference.
P0113. The Application of Invasion Depth Model of Drilling Fluid Particles and Filtrate Jianhua Wang, Wei Zhang, Man Zheng The invasion of particles and filtrate of drilling fluid are the main reasons that cause reservoir damage, however, it is difficult to determine the invasion depth of particles and the filtrate of drilling fluids by using model. In this paper, the mass conservation and radial friction equations are used to establish the invasion depth model of drilling fluid particles and filtrate under dynamic situation with the consideration of internal and external mud cake at the same time. According to the experimental results of low free water drilling fluid, polymer drilling fluid and KCl film forming drilling fluid core return permeability and filter loss amount, the invasion depths of them are calculated. Comparing with the on field skin factor testing results, it is found that the deeper invasion depth and larger skin factor, the greater reservoir damage. The degree of reservoir damage caused by drilling fluid can be estimated by the evaluation result combined with the model calculation, which is significant for studying the reservoir damage mechanism and making protective measures.
P0114. The Transportation Study of Ore-sourced Elements in the Cover Layer with respect to Particle Grades by Varying Coefficient Models Deyi Xu, Qiuming Cheng, Shuyun Xie To understand the transportation mechanism of ore-sourced elements in the thick cover layer above the ore body is useful for detecting deep buried deposits from the weak and slow ore-forming information on the surface. The contents of ore-sourced elements change along the distance from the ore body and with respect to the particle grades as well. To consider both the distance and the grade together, the varying coefficient regression model was applied for the case study. Seventy-eight samples were sequentially collected from a vertical loess drill core above Diyanqinamo Mo-deposit, which were equally separated in 1m’s intervals. Each of the samples was screened into grains of grade 20, 40, 80, 120, 160, 200 and 200+ meshes to get 78×7 graded samples. The contents of 32 elements in each graded sample were measured by a handheld X-ray fluorescence analyzer to form a 78×7×32 dataset. Firstly, since the grain grades as attributes in a panel dataset are in orders, we regarded this dataset as a new type one and named it as Ordered-Attributes Panel Data (OAPD). Secondly, the non-parametric Friedman test was used to explore the correlation of the contents of the elements with both the distance from the ore and the grain grade separately. It turned out that 22 elementsare correlated with both the distance and the grade. Then, varying coefficient-models were applied to the 22 elements to get the variation modes of the 80
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regression coefficients, reflecting the change rates of the contents with respect to the grain grades. At last, the variation modes were sketched and classified, which is helpful for understanding the grain grade related mechanism of the transportation of the ore-sourced elements in the cover layer.
P0115. The influence of image resolution on pore-scale modelling results: a comparison of super-resolution technique and experimental dataset Marina V. Karsanina, Kirill Gerke, Rail I. Kadyrov, Siarhei Khirevich, Timofey O. Sizonenko It is well known that the image resolution affects the results of pore-scale modelling. This problem is also usually reffered to as discretization issue. To narrow the topic of our study, we consider only single phase flow simulation (i.e., permeability) and image quality without the addition of the invisible porosity (i.e., assuming that there is no porosity below the coarsest image resolution). The very same porous media sample was imaged on different resolutions by means of X-ray microtomography. All resulting images were processed to obtain the same region of interest, i.e., representing the same physical volume but with different resolutions. The coarsest image was utilized as input data into stochastic super-resolution method. The comparison of pore-scale modelling results for artificial and experimental images with different resolutions shows that simulated permeability values seem to approach some asymptotic behavior. This enables in future to incorporate structural information on a given resolution for an accurate (descretization independent) prediction of transport properties.
P0116. Trapped gas bubbles in sand: Determining their effect on hydraulic conductivity and CT imaging Tomas Princ, Helena MR Fideles, Johannes Koestel, Michal Snehota The aim of this study was to experimentally determine a relationship between gas residual saturation (SGR) and hydraulic conductivity (K) of two coarse sands and corresponding gas bubbles spatial distribution. Series of constant head infiltration-outflow experiments were used to determine the relationship between the K and the SGR. Air trapping was achieved by repeating drainage and imbibition of water into the initially fully saturated sample. The value of K was determined using a constant head infiltration experiment and evaluated by Darcy’s law from measured steady-state flux. After the first constant head infiltration run and then after each subsequent infiltration run, the sample was drained under tension on a sand tank. The SGR was determined gravimetrically after each infiltration run. Each infiltration run thus provide one value of K(SGR). Four samples were scanned by micro-computed x-ray tomography (CT) to obtain information on entrapped air cluster size, shape and distribution. CT showed that fractures occurred at the bottom of the sample. Therefore, the experimental setup with more rigid support was designed. The fractures were not observed when improved set-up was used for experiments and the K(SGR) relationship was similar to first batch of samples.
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P01. Poster Session A The spatial distribution of air bubbles within the sample, the histogram of air bubble sizes and residual air content were obtained from binarized CT images. It is clearly visible that the bubbles formed in globular cavities in the loosely packed sand. The cavities emerged as a result of sand particles displacement by growing bubbles. Results confirmed the trend of decreasing K with increasing SGR for both sands under study. The highest entrapped air content were detected in the upper half of the sample. The results confirmed that the trend of the K(SGR) relationship was a consequence of changes in entrapped air bubbles distribution.
P0117. Uncertainty in geomodels - a work-package within the GeoERA Project Bjoern Zehner Using advanced geostatistical methods and the concept of uncertainty is common for reservoir models in the oil and gas or the mining industry. However, the use of these concepts is less common for the large regional models which are built by geological survey organizations. Further, the uncertainty in the 3D models has hitherto rarely been communicated to the users of the geological 3D models in a suitable and easily understandable way. Within one work-package that constitutes part of the project GeoERA, which we want to introduce with this contribution, we plan to lay the foundation for developing methods for how this could be done. As a first step, we plan to identify the different sources of uncertainty in geomodels to gain an overview of whether and how they are currently quantified and which further options exist for quantification. The aim is to establish a classification of different types of uncertainties and to collect requirements for their visualization. These different types of uncertainty are then matched against the state of the art methods for uncertainty visualization in computer graphics. We will identify for which types of uncertainty additional visualization methods are needed and provide example data sets for testing new methods.
P0118. Using Sedsim to predict continental-scale coastal response to climate change Cedric Griffiths Between 2003 and 2008 the sedimentation process modelling tool ‘Sedsim’ was used to quantitatively predict changes in sediment erosion and accretion given three plausible climate-change scenarios on the entire Australian EEZ at 2000 m resolution to 2055. The resultant data sets are in the public domain. This was a world-first use of a fully-integrated process model capable of combining sediment movement and production at high temporal resolution in response to wind, waves, tidal currents, storms, cyclones, sea temperatures, carbonate production, and river flow. This enabled coastal developers and insurers to make suitable provision for worst-case scenario planning in the Australian coastal margins. At least one Australian State Government has changed coastal planning regulations as a result of this work. This technology is now available to Coastal States around the world for planning purposes. Among the coastlines most vulnerable to climate-change over the next 50 years are the Baltic States and the Indian and Pacific Ocean islands. Examples of predictive and hindcast modelling are shown from these areas.
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P0119. “8C” criteria of data sources for data mining in petroleum industry informatization Li Dawei Petroleum industry informatization in China has experienced three major stages (decentralized construction, centralized construction and integrated application), and is advancing to full sharing stage. It has become a strong engine for promoting the whole petroleum industry that has entered “big data” era. For petroleum industry informatization, no matter the past “small data” or the present “big data”, data is always the most basic and core part. But we have not fully utilized these data assets, while data mining is an important and powerful tool to deeply utilize the value of “big data” in petroleum industry. However, because of the reasons in technique and management, the related data sources for data mining in petroleum industry informatization still has various problems, such as inaccuracy, out-of-date, incompleteness, inconsistency, insecurity, etc., which has seriously restricted the development and application of petroleum industry informatization. Then, based on my studies and experiences in petroleum exploration�development and informatization for many years, I proposes the “8C” criteria of data sources for data mining in petroleum industry informatization: Correctness, Currency, Completeness, Consistency, Confidentiality, Conciseness, Concentration, and Criterion. This paper discusses the detailed connotations of the “8C” criteria. The idea proposed in this paper has certain significance for data resource construction, application and management level advancement in petroleum industry.
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P0201. Kohonen neural network and factor analysis applied to identify and extract Ag-Au mineralization Xiaotong Yu, Fan Xiao, Yongzhang Zhou The silver-gold deposit has large economic value, and prospecting of silver and gold ore deposits has been considered to be one of the most significant tasks in mineral exploration. The traditional methods for geochemical anomaly identification such as discriminant analysis, cluster analysis, and factor analysis are limited in analyzing big geochemical datasets (Castillo-Munoz and Howarth, 1976; Cheng et al., 1994; Clemens et al., 2002). More applicable methods should be developed and applied in geochemical anomaly recongnization and extraction. The Pangxidong district is located at the southern part of the famous Qinzhou Bay-Hangzhou Bay metallogenic belt (QHMB), which is a very important and giant polymetallic mineralization belt in South China. It is just in the Qinzhou Bay-Hangzhou Bay tectonic suture originated from the collision between the Yangtze and Cathaysia blocks, which has experienced multi-stage orogeny from the Neoproterozoic to the Mesozoic (Zhou et al., 2017; Zheng et al., 2016). Recently, big data mining attracted much attention from geomathematicians. Dimensionality reduction, machine learning, and the like are considered important to identify and extract mineralization imformation from geochenmical exploration data (Zhou et al., 2018). In this case study, Kohonen neural network (KNN) and factor analysis are applied to identify and extract the Ag-Au mineralization in Pangxidong district. Firstly, the geochemical data was classified by using the KNN method. Then, factor analysis was further applied to the classified data. It is shown that the second factor (F2) may be well interpreted as mineralization associated geochemical anomaly asscociated with both Ag-Au deposits and ductile shear zone in the study area. The map of F2 is significant for delineation of target areas for further prospecting potentially undiscovered Ag-Au ore deposits. The hybrid method not only effectively recognizes the mineralization factors hidden in geochemical data, but also indicates the prospecting targets.
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P0202. Petrophysical Analysis Using Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Fractured Granite Basement Reservoir in Cuu Long Basin, Vietnam Huy Giao Pham, Nakaret Kano, Kushan Sandunil Despite the fact that petroleum has been mainly produced in Vietnam from Pre-Tertiary fractured granite basement reservoirs since 1987 petrophysical characterization of fracture systems and the very estimation of fracture porosity of this unconventional reservoir type are not yet satisfactory from both theoretical and practical points of view. On the other hand, for the last several decades the amount of petroleum exploration and production data have been accumulated and become huge. How to do a mining of these data and get better geological information from them is a challenge, for whose solution machine-learning techniques can be of great use. In addition to the core analysis and conventional log analysis results estimation or prediction of fracture porosity can be estimated by soft computing methods. In this study, a petrophysical analysis was successfully conducted to estimate fracture porosity using a machine-learning technique, i.e., Adaptive Neuro-Fuzzy Inference System (ANFIS) for an oil field in the Cuu long basin, southern offshore Vietnam. The well log data sets including gamma ray, resistivity, shallow resistivity, sonic, bulk density, neutron porosity, photoelectric factor, and caliper were collected from two wells BHX01 and BHX02. Fracture porosity calculated using conventional method was found between 0.01 and 2.33 % for BHX01 and between 0.15 and 6.63 % for BHX02, respectively. These values were also used as the target in ANFIS analysis, which could predict fracture porosity between 0.60 and 0.17% for the two zones selected that match well with the fracture porosity estimated based on core analysis and conventional method.
P0203. Short and long term regional and global climate modeling using multi scale Kalman Filtering Migdat Hodzic, Ivan Kennedy Fluctuations of surface temperatures characterise the Earth’s climate. These variations result from imbalances in diurnal and longer term flows of short and long wave energy at the surface. For descriptive, predictive and explanatory purposes, this balance can be conveniently modelled as an electrical circuit, with climate forcing expressed as power per unit area (W/m2), the currents to and from the surface and the top of the atmosphere controlled by its resistance; temporal and spatial variations in resistance are controlled by changes in greenhouse gas (GHG) content, especially of water vapour. The composition of non-GHGs thermodynamically determines the capacitance of the atmosphere to temporally store energy, integrated inductively by variations in temperature gradients and kinetic and configurational entropy. In contrast to the well-mixed atmospheric CO2, water vapour is clearly the major control of local or global conditions, both for greenhouse forcing and for positive feedback. The charge of energy at the surface with incoming short wave solar insolation (ISR(1-A)) determines its black body temperature and the emissive power of outgoing longwave energy (OLR). Absorption of vibrational radiation by GHGs continuously charges the atmosphere with sensible (kinetic) energy. Persistent perturbations in the balance between shortwave and longwave radiations provide long term trends in surface temperature referred to as climate change. A multi-time scale 86
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Kalman filter methodology for smoothing, filtering and prediction of this complex circuit provides a self-correcting approach to short and long term climate changes based on historical data (such as the Vostok data) allowing management of climate change by suitable circuit adjustments, predictions and interpretations. This model will also be applied regionally to the periodic La Nina flooding of Australia’s Lake Eyre as an analogue of the warming effects of increasing irrigation.
P0204. MPS-based Geological Pattern Reconstruction with 2D Cross-sections Hou Weisheng, Tiancheng Zheng, Hengguang Liu Constructing 3D geological model is the process of recognition and reproducing patterns with multiple geological data. Many geologic modeling techniques have successfully shown the lack of geologic realism. Multiple-point statistics (MPS) can capture realistic complex spatial characteristics, as well as incorporating geological knowledge, and has gained much attention in the field of geostatistics. In the most of MPS algorithms, 3D realization usually requires 3D training image (TI) modeled by other algorithms. Although some algorithms were developed with two-dimensional data, some artifacts still appear in the realizations. In this study, a multi-scale iterative MPS algorithm is presented, with four closure geological cross-sections as TIs. Constructing three-dimensional TIs and pattern database is the first step. After downscaling the simulation grid (SG) to the smallest scale, cross-sections located on the boundaries of the SG are expanded to an area termed as “expansion area” where nodes’ attributes are assigned at the same time. 3D patterns can be extracted from the expansion area. Presetting simulating parameters is the second step. The third step is to fill up all unknown nodes along the simulating path. The collections of nodes to be pasted into the grid in pattern size are selected randomly from patterns that found in the pattern database. The last step is to moderate the artifacts based on GOSIM algorithm, in which each scale contains several iterations. In each iteration, the approximately nearest patterns for each node to visit in the SG will be searched for and then the simulation grid will be updated with those patterns. The pattern database will be updated when a new realization is to be generated. The simulation results illustrated that the presented algorithm is stable and effective. Some realizations may sketch plausibly the possible subsurface geological structure. Acknowledge�This research was supported by the NSFC Program (41772345, 41472300).
P0205. Variational Autoencoders in new instances generation tasks Gleb Shishaev, Vasily Demyanov, Alexander Mokryak The aim of this thesis is to propose an approach of new geological instances generation. These instances can be applied as a prior space providing information in a Bayesian analysis for use in reservoir simulation and prediction under uncertainty. The main issue in such kind of generative tasks is how to control a realism of parameters combination describing such instances. Widely recognized methods of uncertainty analysis and quantification are based on Bayes theorem; however, it is hard to estimate the prior information because of lack of geological data and field analogs, moreover, it is hard to control realism
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P02. Poster Session B of such priors. All these become a subject, which is consistent with the subjective nature of uncertainty description. Variational Autoencoder (VAE) is a tool based on neural networks, which is able to provide new objects preserving realism of parameters combination. It doesn’t require any new engineering, just appropriate training data. This approach is dataspecific, which means that they will only be able to provide data similar to what they have been trained on. Autoencoders are learned automatically from data examples, which is a useful property: it means that it is easy to train specialized instances of the algorithm that will perform well on a specific type of input. VAE is rooted in Bayesian inference, i.e. it wants to model the underlying probability distribution of data so that it could sample new data from that distribution. The most interesting feature in comparison with other algorithms in its ability to handle input data of real geological sets and convert it into space of latent variables; such a space can identify some hidden features and dependencies of uncertain parameters without any subjectivity. Finally, VAE uses these latent variables for creation of input analogs, which can exist in nature.
P0206. Remote Sensing Model Construction of Degree of Rock Weathering in the Nujiang Fault Zone Zhifang Zhao, Shucheng Tan, Qi Chen, Lin Luo, Jing Xi, Runhuai Hong, Haiying Yang, Binxian He Degree of rock weathering can be used to distinguish the superiority and inferiority of the geological properties and rock mass quality in crustal surface’s rock mass engineering.At present, the qualitative evaluation method of engineering geology is mostly used to divide the degree of rock weathering. However, the traditional evaluation methods of engineering geology have limitation in the case that outcrop is not obvious or the vegetation coverage is high. The traditional evaluation methods are even harder to be applied in border areas with well-developed fault structures. The remote sensing image is a very good indication of the degree of rock weathering, as it records the information of vegetation coverage, geological structure, and other factors which are related to the degree of rock weathering. This paper selects the Nujiang fault zone located in China - Burma border as study area, using remote sensing technology along with the rock hardness, fracture, vegetation coverage and slope indexes to construct remote sensing model of the degree of rock weathering and proceed the model application. After field verification, the model can represent the regional degree of rock weathering very well. It’s a new exploration of quantitative remote sensing division applied in the degree of rock weathering, providing reference for the degree of rock weathering identification outside the borders.
P0207. Multidomain clustering as a key assistant for history matching Nikita Bukhanov, Gleb Shishaev, Vasily Demyanov, Boris Belozerov History matching as an inverse problem have vast amount of uncertainties to get into account. These uncertainties are spread along all the essential steps of geomodeling. First, raw geophysical data (well logs and seismic) is interpreted and transformed into geological grid, describing the subsurface. Second, spatial distribution of parameters describing rock and flow properties is obtained usually through geostatistics. And third, dynamic data and production profiles build a framework 88
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for forward simulations, which become a basis for solution of inverse problem. All these three steps have different sources of errors (measurement error, algorithm inaccuracy). These errors multiply and progradate through the whole chain of modeling steps narrowing the choice of parameters involving in history matching process. Statistical learning methods may act as glue for data from different domains. Clustering and classification algorithms may combine the well logs, seismic and production profile data in order to establish geologically consistent zonation of the field. Such zonation may be an essential step for transparent optimization procedure for history matching process, eliminating therefore uncertainty connected with variety of algorithms involved in subsurface description.
P0208. Forward stratigraphic modeling of subduction-wedge trench-slope basins: Example from the active Hikurangi margin, New Zealand. Barbara Claussmann, Julien Bailleul, Frank Chanier, Geoffroy Mahieux, Adam McArthur, Sergio Courtade, Per Salomonsen, Bruno Vendeville, Daniel Tetzlaff On active margins, the basin-fill architecture of the subduction wedge trenchslope basins is fundamentally controlled by the subduction processes and related tectonic deformations. Therefore, sediment distribution and stratigraphic architecture are complicated by the interactions between predominant deformation, eustatic changes and high sediment flux. Deciphering the contribution of each controlling parameter may allow us to not only better understand but also constrain the tectonic evolution of the subduction margin. Since around 25 Ma, the westward subduction of the Pacific plate beneath the Australian Plate instigated the growth of the Hikurangi subduction wedge. Its succession of trench-slope basins is mainly present offshore, but it also features the emergence of the trench-slope break onshore, in the Coastal Ranges. The controlling parameters on the stratigraphic architecture of these subduction-related basins can therefore be evaluated by integrating outcrop studies, analysis of well data, recently reprocessed, and newly acquired offshore seismic reflection data. Resulting interpretations were conducted to outline the tectono-sedimentary history of these trench-slope basins based on both Miocene and recent examples. The numerical simulation of the geological processes allows to test different geological scenarios and provides new elements for understanding the close interplay between the evolution of a highly deformed complex domain and the development of associated sedimentary basins. This study presents the preliminary results of numerical forward stratigraphic modeling applied to the active Hikurangi margin using the GPM geological process modeling software (Schlumberger). This forward modeling simulator offers a methodology based on the principle of mass and energy conservation that models the erosion, transport, and deposition of siliciclastic sediments in different geological settings. The resulting digital geological models can be considered a reasonably close approximation to reality being built upon a set of initial boundary conditions (paleogeographic conditions) tied to both the onshore and offshore studies.
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P0209. Improved anisotropic singularity index mapping method in support of mineral exploration Wenlei Wang, Qiuming Cheng, Shengyuan Zhang, Jie Zhao Singularity index mapping methods which can appropriately reflect efficiency of singularity theory on investigating non-linear natures of various geological events has attracted a long term attention. In the context of singularity estimation methods, more than 20 published journal papers discussed development of estimation algorithms and their application. Among these case studies, anisotropic natures of geological features are mostly focused. From previously proposed square, directional, and elliptical window based mapping techniques to recently introduced anisotropic singularity method, accumulation and depletion caused by non-linear mineralization processes within a narrow spatial-temporal interval had been characterized, by which spatial variations of related physicochemical quantities were depicted and interpreted. During the application of the squared window-based method, the estimation is isotropic and may not characterize heterogeneity of physicochemical signatures, appropriately, since it assumes that the signatures within the small areas A(�i�i) or in their vicinity are homogeneous without consideration of directional variations in a local scope (i.e., anisotropy). In IAMG2017 at Perth, we proposed an anisotropic singularity method to rectify this issue. After that, we further noticed that the step number i still cannot be determined, appropriately, although the high and small numbers of i are corresponding to regional and local variations, respectively. As a successor of former study, we further developed and proposed an improved anisotropic singularity mapping method. In this study, we used an elliptical window-based method to estimate singularity index, during which U-statistics was utilized to determine that the vicinity (or step number i) is anomalous or background space. According to this elliptical window and U-statistics based singularity index mapping method, not only anisotropic natures of mineralization related anomalies are characterized, but also the step number i can be determined for better understanding and/or interpreting mineralization processes.
P0210. Modeling and simulation of geochemical reactions dring acid pre-flush to improve conformance control of pH-sensitive polymer flooding Hossein Younesian-Farid, Saeid Sadeghnejad Implementing pH-sensitive polymer is an efficient way to improve conformance control of petroleum formations. pH-sensitive polymer solutions have high mobility in low pH conditions, while, due to their high swelling capacity in higher pH values, their viscosity increases with pH increment. Therefore, they can fill high permeability zones/strata and consequently improve the enhanced oil recovery efficiency. To achieve the desired viscosity of the pH-sensitive polymer, the pH of porous media is regulated by implementing appropriate acid pre-flushing. In this study, we develop a mathematical model to predict acid pre-flush behavior before the main pH-sensitive polymer flooding. We formulate convection, diffusion, and homogeneous/heterogeneous geochemical reactions for a compressible fluid flow in porous media. This model consist of three non-linear conservation equations (i.e., mass balnce, species concentration balance, and Darcy law), which are solved simultaneously in a global implicit approach. We discretize the transport and geochemical reaction terms with a finite difference method by implementing the benefit of the Newton90
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Raphson method. Moreover, code vectorization is applied to decrease its computation time. The introduced model is validated by experimental data available in the literature. In order to achieve the optimum flow conditions and to improve the acid residence time, the injected acid pore volume before the breakthrough time at different flow conditions are simulated, which indicates that Damkohler and Peclet dimensionless numbers, initial permeability, and porosity can affect acid residence time and its depth of penetration. The pH values obtained reveals that weak acids can provide better results for acid pre-flush in carbonate formations. Therefore, implementing these types of acids can be a more efficient way to increase the acid residence time as well as to improve conformance control during a pH-sensitive polymer flooding project. This results in increasing of oil production during the implementation of this novel EOR method.
P0211. Numerical Simulation of Metallogenic Process in Zhuxi Giant Tungsten Deposit Qinglin Xia, Tongfei Li, Guanghui Chen The Zhuxi giant skarn-type tungsten deposit has 286×104t proved reserves of WO3 and is located in Jingdezhen of Jiangxi province of China. The ore-bodies are NE-trending and NW-dipping. In order to guide the deep mineral prospecting work of the deposit, we build a 3D geological model based on AutoCAD, using drill hole, exploration line profile and DEM data. Then, the model is divided into 498491 cells by hexahedrons. On this basis, the FLAC3D software is used to simulate the forcethermal-flow of metallogenic process, and the initial and boundary conditions are set up according to the actual and approximate value. In the process of simulation, every 500 steps is recorded. To squeeze in NW-SE force, the model is shortened from 1%, 2%, 3%, 4% to 5% in this direction, and the ore bearing fluid from hot granite body spread to the surrounding strata by rock voids and fractures, the maximum velocity corresponding respectively is 5.43×10-10m/s, 1.03×10-9m/s, 1.29×10-9m/s, 1.34×10-9m /s and 1.51×10-9m/s. Near the contact zone between the granite and limestone of Huanglong formation, Chuanshan formation and Qixia formation, there is high temperature, and rapid flow of pore fluid. It indicates that this is a favorable area for the mineralization. When reaching the 6000 step, the temperature isoline has become very gentle and stable, so the heat transfer process has ended, and the fluid gathering area also has stabilized, indicating that the simulation is over. Further studies have shown that the numerical simulation results are in good agreement with the geological phenomena observed in the field.
P0212. On the size-distribution of karst depressions: lognormal or power models? Eulogio Pardo-Igúzquiza, Telbisz Tamás, Peter Dowd Depressions in karst terrains include closed depressions, or pits, such as poljes, uvalas, dolines, water pools, grikes, potholes, kamenitzas (solution pans) and other pits on the karst surface. Various algorithms can be used to remove pits from high resolution digital elevation models (DEM) to allow karst depressions to be identified and delineated with a lower size limit determined by the resolution of the DEM, that is, the size of the cell or the DEM pixel in terrain units. One question that arises naturally in the geomorphometric analysis of karst depressions is their size-distribution. Experimental results show that the lognormal and the power distributions provide very good fits to data sets from different karst areas. A
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P02. Poster Session B power distribution is consistent with a fractal structure implying the fractal nature of the sizes of karst depressions which, in turn, may reflect a relationship between size and the structural controls of fracture networks and the fractal fragmentation of the terrain. We have analyzed karst depressions in four different karst massifs in Spain. Two of them are located in the Pyrenees in the north of Spain and two are located in the Betic mountains in the south of Spain. The first two are located at higher altitudes and snow plays a more important role than in the other two in the south of the country. Nevertheless, a power law distribution is a good fit in all cases. We discuss the implications of these results for karst geomorphology and geomorphometry analysis.
P0213. 3D pathway modeling and hydrocarbon migration and accumulation modeling Qiulin Guo, Man Zheng, Jingdu Yu, Wei Yan, Shiyun Mi The purpose of 3D pathway modeling is to provide more effective 3D grids for petroleum migration and accumulation modeling. In order to achieve this goal, we proposed a new method of 3D pathway modeling, including natural grids generation for 3D geological bodies, and the conversion from 3D natural grids to geometric grids of the pathway system. The pathway grid is a type of hybrid grid that comprises volumes (strata), surfaces (fault planes and unconformity surfaces), lines and points. Such grid can be used to describe sand bodies in strata, geometric forms of fault planes and conformity surfaces, and corresponding geological parameters, thus enabling the modeling of hydrocarbon migration and accumulation in the pathway system. Moreover, we proposed a 3D hydrocarbon migration and accumulation modeling method based on hybrid grid of pathway, including a new algorithm to trace petroleum migration from sources to trap. These methods have been successfully applied in the Jurassic formations in the hinterland of the Junggar Basin in western China. Thus, it is believed worth of broad promotion and application in the future.
P0214. A spatial causality method to identify the landslide-induced natural hazard cascades Anne-Laure Argentin, Günther Prasicek, Jörg Robl, Daniel Hölbling, Barbara Friedl Landslides in mountain areas are causing considerable loss of lives and economic damage. Aside from the direct impact landslides have by displacing large amount of soil, they also interfere with the hydrological network, causing river course avulsions and dam formation. Those changes in water flow can have serious impact on human lives. We hypothesize that they can even trigger new landslides far away from the initiating one. We aim to understand the processes at stake in landslide-induced natural hazard cascades over large distances. Since the areas studied are extensive, we use remote sensing data (Landsat). However, this data leaves us with a great uncertainty on possible links between events and on their direction of causality. Satellite images are only available for certain acquisition dates, therefore it is hard to assert automatically what happened between these dates and whether a landslide is responsible for other events. Because of the satellite revisit time and bad weather conditions, several natural hazard events related to the same cascade can occur between two image acquisition dates. The challenge addressed in this conceptual approach is to be able to identify the causality between those events and tell it apart from a spurious correlation. We adapted spatial causality testing for the input of
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geological knowledge to assert the possible causality relationships between natural hazard events.
P0215. Copper ore quality tracking in belt conveyor system using simulation tools Piotr Bardzinski, Leszek Jurdziak, Witold Kawalec, Robert Król The ore quality at mining faces in KGHM underground copper ore mines can be determined based on channel samples and the quality block model built in the Datamine system. Unfortunately, even very accurate information about the quality of the deposit at the mine faces does not translate into the possibility of predicting the composition of the feed (geological or geometallurgical) in the enrichment plants. The mixed ore from faces is loaded cyclically by trucks onto belt conveyors, which convey it to shafts. Along the way, mixed ore from many loading points on conveyors forms a divisional stream, which goes to main haulage conveyors where ore streams from various divisions are combined. The ore can be stopped in bunkers. The way of filling the bunkers and ore flow temporary stoppages increase the degree of ore mixing and affect the change of the sequence of its arrival, which hinders the ability to track its quality changes. The DISIRE project proposed RFID tags to determine the ore composition. A complementary method of prediction of ore quality comes from simulating tagged ore flow. The paper describes the use of the FlexSim program for this purpose. It is possible to identify each ore batch loaded on the conveyor with a virtual tag carrying information about the ore composition and to forecast ore feed composition by simulating ore flow in the transport system. The flow of tags through bunkers can be determined by experiments with tags and simulated with the help of DEM. Forecasts of the composition of the feed for the next shift can be prepared with actual plans of mining division operations, the filling level of bunkers and the work plan of the transport system. Any significant deviation from the plans requires the simulation to be restarted for new operating parameters of the system.
P0216. Stochastic upscaling of hydrodynamic dispersion and retardation factor based on laboratory experiments Vanessa Godoy, Lázaro Zuquette, J. Jaime Gómez-Hernández Hydrodynamic dispersion (D) and retardation factor (R) are key parameters for the prediction of solute transport and their values are frequently derived from laboratory experiments performed at the centimeter scale. In this presentation, we perform stochastic upscaling for reactive solute transport, using data obtained from laboratory experiments, to assess whether upscaling rules exist that produce similar transport results before and after upscaling, and whether the inherent uncertainty associated to the limited knowledge of heterogeneity is not lost when upscaling is performed. The enhanced macrodispersion coefficient approach was used to upscale the local scale D and, as a novelty, the impact of heterogeneity of local dispersivity was also taken into account. To upscale R, a p-norm was used to compute an equivalent R. Uncertainty analyses were also performed to evaluate how it propagates after upscaling. Numerical simulations were performed using the FEFLOW code. Results show that only after the inclusion of a fictitious R the upscaling using a macrodispersion model worked well. When comparing the breakthrough curves for the fine and coarse scales after D upscaling, the best results were obtained for the 93
P02. Poster Session B mean and last arrival times, while the early arrival time was not well reproduced. Uncertainty was correctly propagated after D upscaling, but only when the fictitious R was included. Retardation was well reproduced at the coarse scale only when a specific p-exponent was used for each realization to compute the coarse R from the spatially heterogeneous fine-scale R values. R upscaling propagated well the uncertainty for the early and mean arrival times of the breakthrough curves, while the late arrival time was underestimated. These results show that the stochastic upscaling of D and R can be incorporated into daily practice even using commercial codes, but a correction for the loss of heterogeneity due to upscaling may be needed.
P0218. The relationship between the pacific subduction and fractal dimension of granitoids in Late Mesozoic, Great Xing’an Range, Northeast China Pingping Zhu, Qiuming Cheng Late Mesozoic granitoids are widespread in the Great Xing’an Range (GXR), which is part of a large igneous province in the eastern China. However, the geodynamic mechanism of the granitoids is still in fiercely controversial. There are mainly two models be proposed, delamination and thermal erosion. Compared to the previous study, this paper discuss the geodynamic mechanism from a new persperctive of granitoids’ ages and fractal dimension of its shape. The result shows that the granitoids’ age peaks are gradually older from South GXR, North GXR and Erguna Block (EB) in the Jurassic, and is a opposite trend in the Cretaceous. There is an obviously transition at ca. 145Ma. This suggests that the pacific plate subduct beneath the Great Xing’an Range and had an obviously transition in the late Mesozoic, as the Mongol¬Okhotsk ocean is closure at early Permian. The granitoids fractal dimensions of the perimeter¬area model (D) also show the same spatial features. The Ds are gradually smaller from South GXR (0.6731), North GXR (0.6280) and EB (0.6079) in the Jurassic, and gradually larger from South GXR (0.6096), North GXR (0.6302) and EB (0.6399) in Cretaceous. This implys that the geometrical irregularities of granitoids are shaped by subduction, not the thermal erosion. These characteristics of granitoids could be best explained by the subduction of the pacific plate in the late Mesozoic, the transition of subduct angle from high to low at ca. 145Ma, delamination, the upwelling of the lithosphere, extensive intraplate granitoids and back¬arc spreading. Combined with regional data of the K¬Ar age of subdcting pacific slab, the geometry from 12 Pacific seamount chains, paleogeography, mineral incluisons and geothermal simulation of lithosphere, we reckon that the delamination of the thicken pacific plate is the geodynamic mechanism.
P0219. Using visible near-infrared spectroscopy and imaging to estimate heavy metals in black soils of northeast China Lu Wang, Maozhi Wang, Bingli Liu Black soils are important for the relevance to food security and climate change. This paper developed a fast and convenitent technique combined near-infrared analysis and imaging specroscopy to estimate heavy metals, including Pb, Cd, Hg, As, and Cr, in black soils in the northeast of China. We collected 122 samples in a 300 km2 research area in the west of Qixing farm, Fujin, Jiamusi, Heilongjiang, China. 94
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Firstly, we build spectral and geochemical library of black soil in the research area and extracted the feature bands by the method of correlation coefficient. Then, we use stepwise multiple linear regressions (MLR), partial least squares regression (PLSR) and support vector machines (SVM) to predict heavy metals in black soils. Thirdly, we extract reflectance spectral of black soils samples from CASI/SASI airborne hyperspectral images in the research area and build the quantitative inversion models of remote sensing by the prediction models. At last, we fulfill the mapping of the heavy metals in black soils and compared mapping results with geochemical mapping results.
P0220. DISIRE Experiments of Ore Tracking in the KGHM Underground Copper Ore Mine Piotr Bardzinski, Leszek Jurdziak, Witold Kawalec, Robert Król The idea of identification of copper ore batches on delivery to the processing plants by applying the ore tracking system with the use of RFID tags and real-time transportation system simulation has been investigated by the “Non-ferrous mineral processing” work package of the DISIRE project, launched within the HORIZON 2020 framework. The core experiment of the ore tracking system in the KGHM underground copper mine – annotating the mined ore with RFID tags dropped into the conveyed ore in the selected positions of the vast transportation system and reading tags on the entrance to the ore enrichment plant, was made during regular mining operations of several consecutive shifts. The aim of the experiment was to check whether the dedicated tags and sensors match harsh mining conditions and to provide the real data of annotating ore together with the supplementary data derived from the existing information systems. The high survival rate of tags proved the ability of the use of tags for annotating ore even in the extremely tough environment (large ore lumps, moisture, passage through crushers). Analysis of correlations of dropping and reading sequence of tags and distribution of traveling time of tags against the ore flow simulation results provided the valuable data for tuning the ore tracking system. In the paper the preparation of the industrial tests in the KGHM underground copper ore mine Lubin, tests course and test results are presented.
P0221. BIM-Based Method for Site Investigation in Geotechnical Projects Junqiang Zhang Building Information Modeling (BIM) is one of the most attractive technologies in architecture, engineering and construction industries. With it, an accurate digital model of a building is constructed. When completed, all the relevant data is contained in the model. This model enables visualization, simulation collaboration , which make better decision-making possible within the whole project team. Geotechnical information is critically important in geotechnical construction projects. However�the geotechnical aspect of the BIM model often be neglected in major construction projects. This often result in the delay and overruns especially when the project is infrastructure based. The main reason is the output of geotechnical engineering site investigation is in the form of document reports and geological maps, which can’t be reused in BIM directly. To change this�we put forward a BIM-based method for site investigation in geotechnical projects. The core of this method is DMA. D is the BIM database to manage the lifecycle geotechnical
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P02. Poster Session B data. M is a data-rich�object-oriented and digital model to integrated the geological information in there-dimensional. A is an analysis center from which views and data appropriate to various users’ needs can be extracted and analyzed. In this method, the continuously updated 3D model is a working platform of site investigation. Finally�the combination of the model and the database were taken as the information carrier of investigation output which will be submitted to the following architecture,engineering and construction industries. So the geotechnical data can be shared efficiently. The site investigation of YangFangGou hydropower in Sichuan province of China was used to verify the reliability of the method. The applied case indicates this method can increase the efficiency of site investigation and the quality of geotechnical data, which is important to the accuracy of geology-related decisions in the entire life cycle of building projects.
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T01 Compositional Data Analysis for Geochemical Data Jennifer McKinley, Raimon Tolosana-Delgado Compositional data show the relative importance of the parts of a whole. Typical examples include percentages, ppm, ppb, molar composition and are common in many fields of science, particularly in the geosciences where the compositional nature of data such as geochemical soil or stream water data imposes several limitations on how the data should be analysed and presented. The problems relate to the constant sum problem (closure), and the inherently multivariate relative information conveyed by compositional data. Session submissions are welcome that cover both methodological approaches and developments in the field of compositional data analysis and applied case studies.
T0101. Replacement of values above an upper detection limit in compositions Dominika Miksova, Peter Filzmoser, Maarit Middleton Geochemical compositions are often affected by values exceeding an upper detection limit (UDL). The example deals with plant data, where UDL values are common for K, P, Mn and Zn in ashed concentrations. While several approaches exist for dealing with values below a lower detection limit (LDL), see e.g. Martin et al. (2012), simple approaches for UDL values, such as replacing them by 1.2 times the UDL, are often used in practise. We propose an approach based on Tobit regression to replace UDL values by statistically meaningful numbers. This method follows the ideas of Martin et al. (2012), respects the compositional nature of the data, and can be robustified to cope with data outliers. Simulations and real data experiments underline the usefulness of the method in terms of preserving the multivariate data structure. Finally, also a replacement strategy is proposed to deal with UDL and LDL values in biogeochemical data sets. References: J.A. Martin-Fernandez, K. Hron, M. Templ, P. Filzmoser, and J. PalareaAlbaladejo (2012). Model-based replacement of rounded zeros in compositional data: classical and robust approaches. Computational Statistics & Data Analysis, 56(9):2688-2704.
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T0102. Estimation of regionalised compositions with recovery of original units Vera Pawlowsky-Glahn, J.A. Martín-Fernández, Juan José Egozcue, Ricardo A. Olea Spatial estimation of compositional vectors is nowadays a straightforward, wellestablished practice when working in log-ratio orthonormal coordinates applying a combination of geostatistical and compositional data tools. Representation of the resulting estimates in the simplex, whatever the closure constant, is also straightforward. Difficulties appear when the compositional vector is not closed, namely, it is a vector of strictly positive components carrying relative information, and the modeler wants the estimates expressed in the original units. This is the case, for example, when working with compositions measured in mol per liter, microgram per cubic meter, or when working with non-closed subcompositions. One possible approach is to operate in a T-space: the product space of a simplex with the positive real half-line or similar constructions. In this case, original units can be recovered using an auxiliary variable such as the geometric mean of the parts of the composition, with their sum, or with another variable representing a total. However, these approaches require determination of a proper sample space for the auxiliary variable. Here, we explore two approaches: (a) the possibility of imposing an arbitrary closure constant and to work with the residual component with respect to it; and (b) use of the orthonormal coordinate representation of the composition and the logarithmic mean of the parts as auxiliary variable, thus working in a T-space. Real and simulated data are analyzed to illustrate the performance of the approaches.
T0103. Incorporating analytical errors in log-ratio based compositional discriminant analysis Solveig Pospiech, Raimon Tolosana-Delgado Uncertainties in the measurement of the geochemical composition of various sample materials are rarely included for statistical analyses of the data. In case of log-ratio methods, incorporating errors in the analysis has even not yet been done, up to the authors’ knowledge. Many calibration procedures provide relative cell-wise errors, which can be conveniently combined to deliver error assessments for any set of log-ratios. In this contribution we incorporate all these errors in estimates of the mean vector and covariance matrix of the data on a particular log-ratio. Thanks to the linear/bilinear relation between mean/covariance estimates among different logratio representations, such error-integrating estimates are affine equivariant. These means and covariances are the building blocks of many statistical analysis. Here we focus on developing an error-integrating Fisher rule, but the methodology can be readily applied to other linear models with compositional variables, like regression or ANOVA. In general, results show that the incorporation of errors produce a more conservative (and honest) assessment of the discrimination direction and separability of the subpopulations considered. The application of using cell-wise errors and its impact on interpretation of results will be shown by case studies of geochemical composition of tea plants in relation to geological source rock.
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T0104. Geochemistry of surface waters of the Tiber River basin: Compositional Data Analysis approach Caterina Gozzi, Peter Filzmoser, Orlando Vaselli, Antonella Buccianti The Tiber River catchment is the largest river basin in central Italy, draining an area of 17,156 km2. The roughly NS-oriented Tiber River has a length of 409 km and is located between the carbonatic Apennine ridge to the east and the potassic and ultrapotassic volcanic complexes to the west. After flowing through Tuscany, Umbria and Lazio, it enters, at the end of its course, the Tyrrhenian Sea near the city of Rome. In this study the chemical composition of 160 water samples belonging to both the Tiber River and the main tributaries is investigated using the Compositional Data Analysis approach. The compositional nature of geochemical data is well known in literature (Aitchison, 1986), consequently the concentration of solutes and contaminants in surface waters should be considered in a compositional context, taking into account the simplex with its proper geometry as the natural sample space (Buccianti et al., 2008). Dissolved major species and several trace elements are considered and the results are graphically displayed using biplots to evaluate the geochemical variables that are responsible for data variability. Multivariate outliers for compositional data are also highlighted according to the method Filzmoser et al. (2012). Moreover with the aim of detecting the changes in the chemical composition of the Tiber River waters from the source to the mouth, the robust Mahalanobis distance between pairs of multivariate observations in a compositional context is calculated, as a measure of similarity among samples. This method allows to evaluate the compositional changes and relate them to the bedrock geology, geomorphology, climate and soil use as well as to the anthropogenic impact. Aitchison, J., 1986. Chapman and Hall, London. 463 p. Buccianti, A., Egozcue J. J. and Pawlowsky-Glahn V., 2008. Math. Geosci. 40, 475–488. Filzmoser, P., Hron K., Reimann C., 2012. Comput. Geosci. 39, 77–85.
T0105. Mapping gold pathfinder metal ratios in Northern Nevada, USA - A compositional analysis approach Luis Braga, Jean B.B. Reis The aim of this presentation is to evaluate the methodology developped in a previous work, Braga, L. et al, (2016), based on Pawlowsky-Glahn, V., & Olea, R. A. (2004), to Carlin type gold deposits (CGTD) in northern Nevada, in the United States. We proposed a methodology for mapping gold pathfinder metal ratios as a tool to reinforce geochemical signals. Pathfinder elements associated with CGTDs have led to the discovery of several CGTDs such as Cortez, Jerrit Canyon and Goldstrike in Northern Nevada. By comparing the maps generated by the proposed methodology to known deposits sites we can point out the relevance of the procedure. The data were provided by an open report from the United States Geological Survey (USGS). The basic steps are:1. Exploratory analysis of the data; 2. Calculation of compositions ; 3. Principal compositional component analysis; 4. Selection of subcompositions; 5. Modeling the logratio variogram; 6. Selection of balances; 7. Mapping the interpolation of composition represented by a chosen balance, the intensity of the signal is associated to gold mineralization. All the calculations were done with 99
T01. Compositional Data Analysis for Geochemical Data the package Compositions, Boogaart, K. G., & Tolosana-Delgado, R. (2013) References Braga,L.P., Porto,C., Silva,F. and Borba,R.(2016). Mapping gold pathfinder metal ratios – A methodological approach based on compositional analysis of spatially distributed multivariate data. Abstract 1055 35th International Geological Congress, Cape Town, South Africa. http://www.americangeosciences.org/information/igc Pawlowsky-Glahn, V., & Olea, R. A. (2004). Geostatistical analysis of compositional data. International Association for Mathematical Geology Studies in Mathematical Geology (Vol. 7, 304pp.). New York: Oxford University Press. Boogaart, K. G., & Tolosana-Delgado, R. (2013) Analyzing compositional data with R (258pp.). Hieldelberg: Springer. doi:10.1007/978-3-642-36809-7. Federal University of Rio de Janeiro, Brazil
T0106. Model Construction of Three-dimensional Multi-Fractal Singularity Analysis and Application in Deep Mineral Exploration BingLi Liu, Ke Guo Multi-fractal singularity analysis can reflect mineralization position effectively as the mineralization process is a typical process of singularity. Three-dimensional geological modeling helps to understand spatial distribution of geologic body. Geochemical exploration based on primary halos is able to evaluate the potential of deep mineralization according to the geochemical dispersion patterns. This study intends to combine above three methods in deep mineral exploration: Firstly, constructing the 3-D data volume of primary halo by 3-D visualization modeling and spatial interpolation; Secondly, calculating singularity index and extracting spatial local anomaly based on the model of 3-D multi-fractal singularity analysis; Thirdly, finding enrichment of elements in key areas and extracting local anomaly of section through 3-D profiles cutting; Then, evaluating the potential of deep mineralization through geochemical prospecting criteria result from patterns of spatial distribution of primary halos and laws of local anomalous distribution; Finally, selecting the optimal prospecting target by combination of metalorganic regularity and studying multiple epochs of mineralization. In conclusion, this study applies 3-D visualization modeling, 3-D multi-fractal singularity analysis and primary halo method to develop a methodology for deep mineral quantitative prediction.
T0107. New insights on the self-organizing maps for compositional data: analysis of coal combustion products with an application to a Wyoming power plant Josep A. Martín-Fernández, Mark A. Engle, Leslie F. Ruppert, Ricardo A. Olea The operations in the sample space of compositional data (CoDa) follow the Aitchison geometry. Hence, a methodology consistent with such geometry is required in the statistical analysis of CoDa. Self-organizing map (SOM) is a statistical technique in which a multivariate data set is “mapped” based on the distance among individual data points of interest. SOM can be useful for pattern recognition and interpretation of multivariate data. SOM is more flexible than other techniques in that SOM works with more types of data and allows for classification or mapping 100
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of new data. For CoDa, according to the principle of working on log-ratio coordinates, the simplicial operations and the Aitchison distance are the appropriate elements for a SOM. Economic and environmental concerns are of interest to the understanding of transformations of coal into products bottom ash, fly ash, and economizer fly ash during coal combustion. Because chemical concentrations in the coal combustion products are parts of a whole, they are CoDa. The performance of SOM is illustrated through modeling of the Wyoming power plant data set and comparison to previous results obtained by an alternative non-SOM compositional method.
T0108. A CoDa approach to element chemostratigraphy of the Devonian/Carboniferous boundary Karel Hron, Kamila Fačevicová, Ondřej Bábek, Tomáš Kumpan The Devonian/Carboniferous (D/C) boundary is a critical interval in the Phanerozoic history, which is associated with vigorous climatic perturbations, continental glaciation, global sea-level, rapidly increased extinction rates and lithological signature: Hangenberg black shale and the overlying Hangenberg sandstone. Both layers bear a distinct geochemical signature. Even though either or both of these two lithologies are absent at many sections, their correlative counterparts can be indicated by subtle geochemical markers. We studied elemental geochemistry of fourteen D/C boundary sections in six key areas across Europe with the aim to select globally correlatable elemental proxy for the D/C boundary. Analysis of raw/log-transformed geochemical data (EDXRF, c.p.s. units), presenting the standard approach here, indicates that concentrations of terrigenous elements (Al, K, Rb, Ti and Zr) are mainly controlled by diluted Ca (carried by marine calcium carbonate) in limestone facies and, accordingly, their variations can be related to carbonate production in the sea rather than to terrigenous input from continent. Nevertheless, due to the relative nature of geochemical observations, reliance solely on statistical processing of raw data might lead to incomplete picture of multivariate data structure and/or biased interpretations. For this reason, the aim of this contribution is to discuss the logratio alternatives of the standard statistical methods, which may better reflect the relative nature of the data. For this purpose, principal component analysis was employed to reveal main geochemical patterns and while the geochemical signature of the D/C boundary was further analysed using Q-mode clustering that leads to predicative orthonormal logratio coordinates balances. The comprehensive picture of the multivariate data structure provided by these statistical tools makes them a primary choice for exploratory compositional data analysis. At the same time, it turns out that the standard and compositional approaches have synergic effects.
T0109. Multielement geochemical modelling for pollution in the floodplains – quantifying the spatial relationship Jan Skála Whilst floodplain soils are renowned for their fertility attributable to nutrient inputs, the same enrichment process renders these soils vulnerable to contamination. Various pollution sources and (re)distributional processes may result in zones
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T01. Compositional Data Analysis for Geochemical Data with distinct geochemical characteristics within the catchment. To visualise the catchment scale variability, which arises as a result of these processes, geochemical domains were defined using advisable subcompositions. Principal component analysis adapted to compositions was employed to understand this variability within the geochemical dataset from the Czech monitoring program supplying farmers with information on major nutrients in soil. After a spatial filtering of this dataset, we completed 130 samples with the comprehensive geochemical information on soil trace elements (Be, Cd, Co, Cr, Cu, Hg, Ni, Pb, V, Zn) in floodplains of the Eger River. The compositional covariance structure was then visually inspected for the spatial-suited patterns in the biplot as well as within the selected subcompositions in the ternary diagrams. For this, the data were classified according to the stream distance of the sampling sites from the river mouth. This explanatory practise proved the existence of some underlying spatial patterns of compositions along the river. Hence, we aimed at the quantitative measure of these spatial relations. For this purposes, the Aitchison distance matrix was related to the spatial matrices (stream and Euclidean distance matrices) using the Mantel correlation and the results were regionalised using the Mantel correlogram. The source-related interpretation of soil pollution implied that spatial connection and potential sources affinity were the main drivers of similarity in the observed patterns in the flood-prone soils. The study was supported by the Grant Agency of the Czech Republic - Project No. GA17-00859S.
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T02 Machine Learning for Geoscience Modelling Vasily Demyanov, Mikhail Kanevski The session aims to demonstrate how machine learning solves the present day challenges in geosciences applications. Machine learning has gained the increased attention with more data becoming available to describe natural systems in the digital era, such as environmental and climate modelling, pollution and natural hazards mapping, subsurface reservoir characterisation and optimisation, etc. Learning based techniques have proven their power in making prediction and gain understanding of the geoscience systems behaviour based on “big data” and knowledge integration. Machine learning methods are often seen as complementary/competitive to geostatistics in their flexibility in modelling data and knowledge integration. Many geosciences research groups across the world are using data driven algorithms for various applications. The session will invite multi-disciplinary contributions that will demonstrate recent advances in applying learning based algorithms to geosciences problems.
T0201. Clustering of environmental data using local fractality concept and machine learning Mikhail Kanevski, Mohamed Laib, Fabian Guignard Clustering is one of the most important tasks in data mining. Many methods have been developed to cluster data, including classical k-means type models, density- and grid- based algorithms, kernel based methods etc. Environmental data, describing real natural phenomena, like natural hazards, environmental risks, and renewable energy resources assessments are multivariate and high dimensional. In the present study a method, using local fractality concept, namely sandbox counting method in a high dimensional feature space, and self-organizing maps are applied to detect clusters in environmental data. Sandbox counting is a well known method in fractal dimension estimation. Briefly, it works in the following way: around each data point (k), a number of data points falling into the hypersphere of a radius R is computed. By changing the value of R, a local growth curve N(k,R) for each data point is constructed. Traditional sandbox fractal dimension is estimated by averaging all the curves and estimating the slope of the Log(N) vs. Log(R) dependence. In the present research an averaging is not performed. Instead, self-organizing (Kohonen) maps are used to cluster the curves and to detect patterns in data. The method is applied to the simulated and real multivariate data (sediments pollution, wind fields, permafrost) case studies. The results are quite promising and the method proposed can contribute to a variety of existing tools used for the unsupervised learning of complex high dimensional and multivariate environmental data. 103
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T0202. Domaining with Decision Trees and Geostatistical Simulation Gunes Ertunc, A. Erhan Tercan Homogeneous geological domains can be defined as mineralization zones in which geologic and grade variables exhibit similar properties. Consistent grade and tonnage estimates can only be obtained in such domains. The modeling of these domains is usually carried out by explicit methods which is time consuming and becomes challenging when new samples are introduced. In practice, rapid and objective methods are needed to model homogeneous geological environments. In this study, a new method for modeling homogeneous geological environments in mineral resource estimation has been developed. The method is based on the combination of C4.5 decision tree and sequential Gaussian simulation methods. Decision trees develop a classification tree considering geologic categorical variables with multivariate grade distributions while geostatistical simulations generate a number realizations of block model with multivariate grade distributions. In the final stage the block models are fed into the classification tree and the most frequently observed category is considered as the category of the block. The developed algorithm is tested in Jura data set and then is applied to a rare earth element deposit. The case studies show that the classes produced by proposed method are in good agreement with the classes of the raw data. Keywords: Decision trees, sequential Gaussian simulation, geological domaining
T0203. Integration of geologically interpretative features into machine learning facies classification Julie Halotel, Vasily Demyanov The work presents an automated learning based approach to facies classification task, which is traditionally performed by a geologist/petrophysicist by assigning lithofacies according to wireline log responses and/or core data available. Such manual interpretation can often be subject to biases as the interpretation may vary from one person to another. Additionally, this manual interpretation becomes very laborious and time consuming with very large data sets, such as for giant oil or gas fields or intensively explored basins. Therefore, the automation of the facies classification process with Machine Learning (ML) classification is seen as an intuitive way to facilitate facies interpretation over the bulk of data and make sure some possible alternative scenarios in facies modelling are not overlooked. We propose to enhance the power of the state-of-the-art ML classifiers with a geological insight to improve the performance of purely data-driven classifiers and to make the predictions more geologically consistent. The geological constraints are implemented as additional input variables derived from the wireline log data based on the geological expert knowledge and understanding of the depositional processes. The variables were constructed based on the identification of key features within some of the facies that have unique, typical, and identifiable wireline log responses. The variables were subsequently used in the ML algorithm both as a single input and as several binary inputs. The case study compares two ML supervised learning classifiers – Random Forest and Support Vector Machine – applied to the real field data from Southwest Kansas, USA. Concurrently to the aforementioned enhancing of classifiers, we aim at identifying the key feature(s) which impacts most the predictions in terms of accuracy and 104
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geological consistency. The study demonstrates the value of incorporating geological aspects into ML facies classification.
T0204. Automated lithofacies classification of the Jurassic sequence using machine learning on a large structured well database Harald W. Bøe, Kristian B. Brandsegg, Kenneth Duffaut, Alenka Crne Automatic classifications of well logs using machine learning techniques has gained improved attention within the last couple of years for increasing the accuracy and speed of lithofacies prediction on large datasets. A supervised machine learning methodology written in R combines raw well logs, CPI’s and depositional environment interpretations to automatize lithology related parameters. The method utilizes the XGBoost algorithm, a gradient boosting library with emphasis on computational speed and model accuracy. The study area is the prolific quadrant 30 in the Norwegian Northern North Sea, with around 120 available wells. Raw well log data are preprocessed in order to obtain a consistent database. Further they are quality assured in a manner to flag bad log intervals, washout zones and casing shifts. Geological features such as mudstone, muddy sandstone, sandstone, carbonates and coal are consistently predicted in around 30 of these wells from in-house Exploro petrophysical assessment and used as training data. Depositional environment interpretation is based on sedimentological description of cores, seismostratigraphic observations, and completion well reports and is used as input parameter and as quality control of the training data. The remaining available wells in the fluvial to marine Jurassic sequence within the quadrant 30 are assessed with machine learning predictions. These results are applied in both within well and across well comparisons in order to outline variations in the depositional environment within the study area. The methodology shows merit in consistent categorization with high level of detail in a short time frame. The structured machine learning technique provides a supplementary tool for localizing lithological heterogeneity, defining stratigraphic intervals with similar petrophysical characteristics and changes in water saturation.
T0205. Neural network classification to improve geological and engineering understating for more reliable reservoir prediction Elena Kharyba, Vasily Demyanov, Andrey Antropov, Luka Malencic, Leonid Stulov Reliability of reservoir predictions relies on adequate description of reservoir geology described in a model. For instance, how many lithofiacies/rock types with distinctly different storage/flow capacity are needed to adequately represent the reservoir properties . Reservoir models are conditioned to the petrophysical data that are derived from multivariate observations and are subject to uncertainty in facies interpretation. Present work aims to demonstrate how machine learning classification helps to identify geologically meaningful facies to make reservoir flow simulation model more consistent with the production data. We have used machine learning classification to identify and distribute facies interpretation based on wire log data that reflect different aspects of reservoir rock characteristic – density, porosity, shaliness etc. The study was performed on a large field with hundreds of production wells,
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T02. Machine Learning for Geoscience Modelling which still bears a great deal of geological uncertainty due to the lack of sure lithofacies interpretation from core samples. The reservoir constitute of highly heterogeneous clastic rocks of Miocene and carbonate rocks of Triassic. The Miocene sediments are conglomeratic with very good reservoir properties. The reservoir also contains heterogeneously distributed impermeable low quality rock that acts as barriers/baffles to flow. Unsupervised neural network classification clustered the well data from the multivariate wireline logs (gamma-ray, density and neutron) and provided a spatial distribution of the interpreted rock types. The cluster distribution map demonstrates a good agreement with the reservoir production data from the wells. The interpreted spatial clusters correspond to the zones of different speed of water front movement, which depends on the variation in porosity/permeability relationship for different rock types. This results is very important to understand the water saturation dynamics to make further development decisions on infill drilling to target upswept oil reserves.
T0206. Methodology of fast well log interpretation based on deep Learning models Alexander A. Reshytko, Maria Golitsyna, Dmitry Egorov, Nikita Bukhanov, Artyom Semenikhin, Oksana Osmonalieva, Boris Belozerov The task of reinterpretation of the reservoir given a renewed petrophisical model is a common one and usually it takes a lot of expert time to do. We propose the statistical method, based on deep learning algorithms that can predict probabilities of oil saturation on given well depths. This method makes it possible to make reinterpretation of well logs significantly faster, compared to a human expert. It is based on Bidirectional LSTM Recurrent Neural Networks, that model well log data as a stochastic process, which allows to consider the context well log information alongside with a local one. The output of a model is further post-processed via the procedure of probability calibration by means of an isotonic regression. As a result, the trained model can predict probabilities of oil saturation on given well depths. Moreover, if the trained model is used to interpret well logs, that have interpretation already, the difference between model predictions and manual interpretation can uncover missing intervals. This, in tern, can increase oil in place estimates and can bring significant value to the company. This methodology was tested on a reservoir in Western Siberia and gave promising results.
T0207. Stochastic Simulation with Generative Adversarial Networks Lukas Mosser, Olivier Dubrule, Martin J. Blunt Generative Adversarial Networks (GANs) are a Deep Learning approach for generating two- or three-dimensional images that are ”representative” of an existing set of training images. More specifically, GAN-generated images are such that their multi-dimensional probability density function (pdf) is equal to the implicit pdf associated with the set of training images. GANs transform a latent random vector into a simulated image thanks to a convolutional neural network called the Generator. Then the generated image is fed into another convolutional neural network , the Discriminator, which evaluates whether the Generator’s image can be accepted as representative of the set of training images. Numerous iterations are required until both the Generator and the
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Discriminator are trained in such a way that the images obtained by the Generator are indistinguishable from the images of the training set by the Discriminator. Once training is completed, the trained Generator can be used to generate synthetic images using any realization of the random latent vector as input. Based on the success of GANs for generating two-dimensional images, it was natural to test this approach as a possible alternative to non-conditional simulation of three-dimensional geological objects. The results obtained for the generation of micron-resolution porous media models show that GANs produce outstanding results. In addition, thanks to the mathematical formalism of GANs, it is easy to generate conditional simulations such as that of three-dimensional porous media constrained by two-dimensional cross-sections. The method also generalizes to conditional simulations constrained by wells at the reservoir scale. The method still needs improvement for the generation of images following a multimodal pdf.
T0208. Probabilistic inversion using forward models based on Machine Learning Thomas Mejer Hansen, Knud Skou Cordua, Tue-Holm Jensen Sampling approaches to solve probabilistically formulated inverse problems, are hampered by their high computational demands. This is not least related to evaluating the physical ‘forward model’, i.e. the evaluation of the expected physical response from a subsurface model. We propose to replace a numerical complex evaluation of the forward problem, with a trained neural network that can be evaluated very fast. The training data set is develop by computing the forward response from a large sample of a prior model in form of a geostatistical model. This will introduce a modeling error that is quantified probabilistically such that it can be accounted for during inversion. This allows a very fast and efficient Monte Carlo sampling of the solution to an inverse problem without imposing any unwanted biases. We demonstrate the example related to travel time computation and waveform modeling.
T0209. Influence of input data quantity on accuracy of reservoir properties prediction with machine learning algorithms Dmitry V. Egorov, Nikita V. Bukhanov, Boris Belozerov During the last years interest to application of machine learning methods for a wide range of prognosis problems grows intensively. Prediction of oil and gas reservoir properties is one of possible tasks which can be solved with these algorithms. One of the main benefits of machine learning approach in comparison with more traditional methods such as geostatistics is no need for a variogram analysis and expert geological knowledge. An algorithm can find complex relationships between geological properties and their spatial distribution, its output result is based on the input information only and is not affected by variogram parameters. The problem is that prediction accuracy strongly depends on the amount of input data and distance between input and target points. Evaluation and analysis of these relationships were the main objectives of this research. The study was performed on Shestakovo 3D synthetic geological model. The latter is based on a fluvial reservoir outcrop located in Western Siberia and variogram ranges from analogues. Each cell of the model contains synthetic lithology 107
T02. Machine Learning for Geoscience Modelling log which was used as input and output information to build algorithm, tune it and check prediction quality. Net-to-gross ratio, sand thickness and compartmentalization were target geological properties for prediction. Different parameters (e. g. number of neighbor wells, average distance to prediction point) and their influence on the forecast were estimated to evaluate machine learning approach ability to determine relevance of spatial data. Performed tests showed relative standard deviation of prediction about 15% depending on amount of input data. Comparison of synthetic, based on geostatistics, and predicted properties distribution maps demonstrated capability of examined algorithms to recover geological realism and spatial dependencies which in this case were specified by a variogram. Produced results confirmed considerable potential of machine learning approach as a powerful reservoir characterization tool.
T0210. Efficient uncertainty quantification of reservoir productions by stacked autoencoder-based clustering Kyungbook Lee, Taehun Lee, Jaejun Kim, Byeongcheol Kang, Changhyup Park, Hyundon Shin, Jonggeun Choe Numbers of reservoir models with equivalent probabilities are created by geostatistics to assess reservoir uncertainty. The easiest way to evaluate the uncertainty is to perform a reservoir simulation for hundreds of reservoir models, but simulation cost is too high. Recently, distance-based clustering (DBC) have been used as efficient uncertainty assessment. The purpose of DBC is a classification of reservoir models with similar production behaviors into the same group. Because the models in the same group have similar reservoir performances, simulating a representative model for each group will give the same uncertainty range as simulating the entire model. For DBC to be successful, distance definition, which represents non-similarity between models, is the key factor. In this research, after the main information of reservoir model is extracted through stacked autoencoder (SAE), which is one of deep learning algorithms, the 2-norm of feature vectors is defined as distance to measure the dissimilarity of two facies models. The proposed method is applied to 2D channel reservoirs, which are generated by four training images to consider geological uncertainty in channel direction. At first, parameters in SAE are analyzed by sensitivity analysis to optimize a parameterization from facies models to a feature vector. Uncertainty results are sensitive to the number of neurons and hidden layers as other artificial neural network algorithms, but are stable for the number of clusters. After SAE-based clustering, only 20 representative models can realize uncertainty of 800 prior models. If there are observed dynamic data, the best representative model can be determined. The best model and its 9 surrounding models in the feature space are selected for the qualified models among the 800 models. Only nine additional reservoir simulations can dramatically reduce the initial uncertainty range without inverse algorithms. This research is supported by the project of KIGAM (GP2017-024) and MOTIE (20172510102090).
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T0211. GemPy: Towards high dimensionality problems in structural geological modeling as Bayesian inference Miguel de la Varga, Florian Wellmann The representation of subsurface structures is an essential aspect of a wide variety of geoscientific investigations and applications: ranging from geofluid reservoir studies, over raw material investigations, to geo-sequestration, as well as many branches of geoscientific research studies and applications in geological surveys. Geological modeling has to deal with scarce uncertain data. Therefore, the use of domain knowledge—based on mathematical interpolations and physical laws—is key to reduce the dimensionality of the problem. We present GemPy a full opensource geomodeling method, based on an implicit potential-field interpolation approach and designed to exploit the last advances in machine learning algorithms to perform Bayesian inferences. Bayesian statistics have been used successfully in the field of geophysics and, increasingly, in structural geology. However, despite the exponential growth in computational capabilities, the applications of these methods have been limited to relatively simple problems of low dimensionality and simple mathematical formulations. The exploration algorithms have undergone a significant development especially in regard to Hamiltonian Monte Carlo and Variational methods. The implementation of these algorithms requires the estimation of gradients for parameters of the entire model what increase the mathematical complexity. Due to this, GemPy is built on top of Theano for optimization and evaluation of mathematical expressions to enable symbolic differentiation. With this approach, we aim to reduce the parametric space and significantly reduce the number of iterations required to evaluate a meaningful statistics of the posterior distribution. We consider this aspect an important step forward in the combined analysis of geological and geophysical inverse problems of higher dimensionality.
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T03 Predictive Modelling of Resources and Hazards: Reliability and Uncertainty Andrea G. Fabbri, Emmanuel John Carranza With the ever increasing availability of personal computer facilities and digital imagery it has become attractive to construct spatial databases. They were mostly focused on mineral and energy exploration but eventually they were directed to the assessment and prediction of natural hazards and consequent risks. Spatial prediction modelling of future discoveries or of hazardous occurrences, however, has proven to be a complex challenge requiring mathematical models, assumptions, scenarios and suitable databases. Furthermore, many applications developed to date show that a great variety of processing strategies employed provide results that are difficult to visualize, interpret and compare. This session aims at debating the reliability, uncertainty and comparability of the results of predictive modelling, the prediction patterns. Hopefully a generalized framework and procedural strategy can be identified and databases shared for exhaustive experimentations. Some examples of issues requiring attentions are: (1) Data mining of hidden supporting evidence; (2) Establishing the “footprints” of a study area; (3) Training areas versus study areas; (4) Effectiveness of prediction patterns: number of classes and their uncertainty; (5) Model complexity versus study area complexity; (6) Continuous field versus categorical spatial evidence; (7) Relevance of spatial resolution in modelling; (8) Standardization of parameters and strategies of processing; (9) Interpretation of prediction patterns in physico-chemical terms or land use requirements; (10) Adjustment of spatial relationships by expert knowledge; (11) Comparison of models and prediction patterns; (12) Advanced software for spatial prediction modelling.
T0301. Mineral occurrence target mapping: a general iterative strategy in prediction modelling for mineral exploration Andrea G. Fabbri, Chang-Jo Chung Generalized procedures are discussed to construct target maps ranking the likelihood of future discoveries: for instance, of discoveries like gold occurrences, knowing location and spatial context, of a set of genetically-related gold vein deposits. A favourability modelling process is iterated with a subset of the known occurrences and the resulting prediction patterns are cross-validated with the distribution of the left-out occurrences. The target map originates from integration of all prediction patterns from the iterations. Rank-based statistics related with the target maps 111
T03. Predictive Modelling of Resources and Hazards: Reliability and Uncertainty should provide measures of quality, robustness and uncertainty of the classification of a study area into likelihood of discovery. Much of this is a relatively new area of research, so that to interpret such uncertainty is still a challenge. Models of capturing and integrating spatial relationships between occurrences and their spatial context, and different strategies of iterative cross-validation offer a generalized methodology of spatial prediction and enquiry. Examples of iterations are those of sequential exclusion of occurrences and the ones of random selection. The target maps and the associated statistics become essential to interpret the predictive suitability of the study area. A spatial database developed for advanced training is used to generate target maps. It comes from a study in the Red Lake area in northern Ontario, Canada. It contains information on 37 gold vein deposits whose neighbourhood distribution is instrumental to establishing spatial relationships with the units of categorical thematic maps (bedrock geology, metamorphism and carbonate alteration) and continuous-field maps (geophysical, geochemical and distance functions from faults, unconformity, felsic and mafic volcanic rocks, iron formation and altered geochemical samples), all digitized with 40 m spatial resolution. The experimental results point at the extraction of significant properties of the spatial data that cannot be ignored but that we have yet to master to substantiate the reliability of prediction patterns.
T0302. Prospectivity mapping for porphyry copper-molybdenum mineralization in the Gobi desert covered area, Eastern Tianshan, China Fan Xiao Since circa 1980, mineral exploration and prospecting in the Chinese Eastern Tianshan has successively discovered more than ten porphyry copper-molybdenum polymetallic mines including Tuwu and Yandong giant ore deposits. Hence, the Eastern Tianshan district has been developed to be one of the most significant porphyry copper-molybdenum polymetallic metallogenic belts and copper production resource bases in the Northwestern China, though it has been extensively covered by Gobi-desert (i.e. Tertiary and Quaternary sedimentary deposits), which is considered to be largely hinder for both geological mapping and mineral exploration in this region. In the past several decades, to prospect more economic porphyry copper-molybdenum polymetallic ore deposits in the Gobi-desert area of Eastern Tianshan district, many geological, geochemical and geophysical studies supported by both governments and mining companies have been carried out. The data from these valuable studies definitely benefit for guiding exploration activities of porphyry copper-molybdenum polymetallic deposits in the Eastern Tianshan metallogenic belt, because they could provide much important information such as genesis and prospecting model, geochemical and geophysical characteristics for the more detail investigations of porphyry mineralization in this area. In this study, to comprehensively use the precious and useful regional-scale geological (1: 250 000 structural and geological), geochemical (i.e. 1:200 000 stream sediments geochemical), and geophysical (i.e. 1: 200 000 Bouguer gravity and aeromagnetic) datasets for supporting future exploration of potentially undiscovered porphyry copper-molybdenum polymetallic ore deposits, the fuzzy weights of evidence method has been employed to integrate these data for mapping prospectivity of potential porphyry coppermolybdenum mineralization in Gobi desert covered area, Eastern Tianshan.
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T0303. Integration of 3D geostatistical models of lithology, physical properties and element concentrations for metal deposit imaging in a seafloor hydrothermal vent area Vitor Ribeiro de Sá Seafloor hydrothermal fields account for important source of economic mineral deposits called Volcanogenic Massive Sulfide (VMS) types. These deposits ascribe to major sources of Zn, Cu, Pb, Ag, and Au and significant sources of Co, Sn and so forth. Japan has high demand for these mineral resources. This research aims to clarify target areas where the occurrence of these mineral is more likely to be found in an offshore active hydrothermal area. For this purpose, an area, roughly 150 km from Okinawa was selected as a case study, and stochastic geostatistical techniques were performed, such as Turning Bands (TB) and Truncated Gaussian (TG) simulations. The outcomes of these approaches enable to evaluate different scenarios by considering the uncertainty innate on each realization and previous geological assumptions. Electrical Resistivity (ER) and Gamma Ray (GR) data at drillholes are used as input data for the TB simulations. Both physical parameters act in concert to indicate locations where the ore body might be hosted. In addition, TB is applied to evaluate the distribution of the major and associated elements of this type of deposit. The geochemical data from ICP-MS analysis of core sample from study area are used as input data to generate the realizations. Regarding to TG simulations, the lithological description of collected samples is used as categorical input data, providing relevant information on the anomalies of physical properties with distribution models of sulfide and altered minerals. The results of this research consist of 3D geostatistical models that bring light not only on the target exploration areas but also may clarify mechanisms of transport and deposition of the metal resources.
T0304. Multifractal modeling of worldwide metal size-frequency distributions Frits Agterberg The worldwide size-frequency distributions of several metals including copper, lead, zinc, nickel, molybdenum, gold and silver satisfy a Pareto-lognormal model. Amounts of metal in most of the mineral deposits containing these metals are lognormally distributed, but their largest and smallest deposits form Pareto-type tails. From a mathematical statistical point of view, both the lognormal and the Pareto frequency distribution are “stable” in that asymptotically they can be regarded as end products of many different types of causative processes. More than 99% of metal occurs within the range of the upper tail Pareto distribution plus the upper (> median) part of the central lognormal distribution. Canada-wide size-frequency distributions for copper, zinc, gold and silver, for which relatively many data are available, can be used for comparison of regional central lognormal distributions with their equivalent worldwide size-frequency distributions. For these four metals the logarithmic means differ by only between 1% and 3%, but logarithmic variances are between 50% and 75% less than equivalent estimates for the worldwide sizefrequency distributions. The upper tails of the four Canadian metal size-frequency distributions satisfy the Pareto model with estimated Pareto coefficients approximately equal to those for the worldwide distributions. Deposits for the metals considered can be regarded as the final product of a cascade process consisting of 113
T03. Predictive Modelling of Resources and Hazards: Reliability and Uncertainty self-similar steps resulting in multifractals. The simplest type of multifractal is a model in which metal concentration values for halves of blocks have ratios that are independent of block size. The worldwide lognormal size-frequency distribution of uranium can be explained by means of this simple model. Further assumptions are needed to explain the Pareto tails of the size-frequency distributions for other metals and the fact that, worldwide, the Pareto frequencies for the largest deposits are systematically lower than their central lognormal frequencies.
T0305. Quantifying Uncertainty on 3D Geological Surfaces Using Level Sets with Stochastic Motion Liang Yang, Jef Caers In many geoscience applications, prediction requires building 3D models that are complex and cumbersome. As a result, often a single model, or some small variation of it, is built. Uncertainty is provided by generating many realizations, as sampled from some posterior distribution by Monte Carlo. Built into this common notion of modeling lies a fundamental inefficiency: building many high-resolution models, each one of them being a poor approximation of actual reality and unable to predict new observations. In this paper, we provide a fundamentally new view on the same problem. Consider some geological geometry whose uncertainty needs to be quantified based on geological understanding of rules and data. Instead of modeling its uncertainty with many high-resolution models drawn by Monte Carlo, we will model the uncertainty by means of stochastic motion. This simple idea is as follows: where the geometry is well-known (e.g. near a sample location), the geometry should not “move” much, while away from any data, movement is more uncertain, hence the geometry is less constrained. To model 3D surfaces (e.g. faults, horizons, ore bodies) and their uncertainty, we will integrate this stochastic velocity into the level set equation. Level sets are an ideal way to represent complex surfaces without explicit grid-representations, thereby having the advantage of easily handling any topological changes, which naturally occur because of uncertainty. Our 3D velocity will be generated by a Markov chain sampling of a Gaussian process. This Markovian-type sampling will lead to an increase in efficiency over naïve Monte Carlo. It produces continuously evolving surfaces, which has the useful property of helping to update surfaces with new data. We will illustrate this new idea with simple synthetic examples, as well as a copper deposit case study, where hundreds of boreholes constrain an ore body geometry with 7 different lithologies.
T0306. Radon priority areas as random objects Peter Bossew Indoor radon (Rn) is acknowledged a health hazard. Therefore, it has increasingly been subject to regulation; In Europe, most importantly the EU directive on basic safety standards for protection against ionizing radiation (BSS) which requires delineation of Rn priority areas (RPAs), i.e. areas in which action related to Rn prevention should be taken with priority. Based on the vague BSS definition, calling RPA an area where in a significant number of houses Rn concentration exceeds the reference level, an operable definition is developed. Its core is a Rn measure M, e.g. the mean over a geographical unit or the probability that within the unit indoor Rn (C) exceeds a reference level. Once defined, the RPAs are estimated from data, primarily measured indoor Rn data, but proxy quantities may be required additionally, e.g. geology, ground Rn concentration, geochemical concentrations, ambient dose rate. The decision about 114
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whether a geographical unit is assigned RPA, or in the case of multinomial definition, which class is assigned to that unit, is a classification problem. If estimated from secondary quantities, the task is cross classification. Being results of estimation, RPAs are random objects: a) whether a unit is labelled RPA and b) the topology of a contiguous set of RPA labelled units are uncertain. Here, we investigate (a) for the case of RPA estimated by cross-classification from the geogenic Rn potential (GRP) as secondary quantity. Uncertainty has four sources: (1) imperfect relationship between M and GRP leads to cross-classification uncertainty; (2) uncertainty of GRP, estimated by a geostatistical procedure; (3) uncertainty of M; (4) observation uncertainty of the data C and RP. Here we ignore (4) and concentrate on (1-3). As measure of classification uncertainty, we determine the rate with which given the uncertainty distributions, a spatial unit is labelled differently from its central estimate.
T0307. Could immersive visualization improve geological hazard perception? Hans-Balder Havenith Geohazard research requires extensive spatiotemporal understanding. Currently, most types of hazards can be assessed but representing them in an adequate multiscale space-time frame is limited by existing modelling capabilities. Few applications use integrated geomodels with temporal data representations. We believe that a full geohazard assessment is only possible inside an environment that is in the same time multi-dimensional, spatiotemporal, integrated, fully interactive, immersive and collaborative. Virtual reality technology offers attractive solutions to create such a space. However, also this technology does not automatically provide a platform allowing us to embrace all complex geohazard problems, while being fully immersed in a virtual geoenvironment. The review of geoscientific applications using virtual reality tools shows that some solutions have already been developed many years ago, but widespread use was not possible, partly due to the limited accessibility of the required hard- and software. This is clearly changing now and it can be expected that new solutions should appear soon while it still may remain unclear if virtual reality technology really improves the understanding of geohazards or other geo-processes. Testing the possibly enhanced perceptive capabilities probably requires a new type of cooperation, the one between geoscientists and sociologists and psychologists.
T0308. The fractality of landslides’ spatial association with spatial factors Emmanuel John Carranza, Renguang Zuo It is instructive to quantify the relative significance of landslide spatial factors (e.g., geological structures, geomorphological features, man-made features, etc.) in order allot weights to spatial factors used in landslide susceptibility mapping. This presentation introduces a new method, based on the fractal theory, for quantifying the spatial association of landslides with spatial linear factors and for allotting weights to landslide factors typically used in mapping of landslide-prone areas. Thus, the power equation �=C�–d (where C and d represent, respectively, a constant and fractal dimension), which describes the spatial association of � (landslide density) with � (proximity to spatial factors), is modeled. Significant spatial associations of landslides with spatial factors are characterized by values of d greater than 0, whose
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T03. Predictive Modelling of Resources and Hazards: Reliability and Uncertainty magnitudes signify the strengths of landslides’ spatial associations with corresponding spatial factors. The efficacy of the proposed fractal measure of landslides’ spatial association with spatial factors is demonstrated using two case studies in southern China.
T0309. The model of cyclic exogenous processes and natural risk assessment Alexey Victorov The aim of the research is the probabilistic modeling of cycling exogenous processes for natural risk assessment. We call the cyclic processes those exogenous processes which are characterized with the repeating sequence of changes (landslides for instance). This process is not strictly periodical one but it has got some features of periodicity. The activation of the process is followed by the stage of its recovery, such as microrelief recovery, vegetation and soil cover recovery. The case of a complex cyclic process is considered. The complex cyclic process is a cyclic process, the activation of which occurs under the influence of two groups of factors: local factors activating separate sites (local rains for instance) and “global” ones acting within the whole area like earthquakes. The analysis shows that the development of the exogenous process can be regarded as a change of states with constant transition probabilities determined by the distribution of the period of activation of the process. Thus, we can use Markov chains with a countable number of states as a base for the modeling. It is possible to show that the given chain is an ergodic one and get the equations for final probabilities of states. The conclusions: - using the number of foci of the process in different stages of recovery, it is possible to obtain the distribution of the activation period of the analyzed process without stationary observations and the natural risk assessment; - an area under cyclic exogenous process is in a dynamic balance state; it results in the preservation of the constant quantitative relationships of the process foci in different stages of recovery. The model is illustrated using a landslide database of Seattle (USA) and thermokarst plains with fluvial erosion of Siberia. The research is done with help of RGO and RFBR, grant # 17-05-41141.
T0310. Probabilistic modeling for transport and communication network purpose in the taiga zone Olga Trapeznikova Designing and optimizing local transport and communication networks need analysis of local settlement patterns. Mathematical models are among the most effective and widespread analytic tools for settlement analysis. However, most models are not able to take into account the natural environment, which plays a large role in rural settlement distribution. Our approach stems from the initial agricultural purpose of rural settlements. Our research shows that the severe nature environment of taiga zone is responsible for uniformity of agriculture all over the area and the extreme selectivity of nature units suitable for farming. Thus, the agricultural landscapes and corresponding settlements patterns within the taiga zone differ mainly in their spatial organization, related to their landscape location and each of them needs its own mathematical model. As an example let us examine a model of porechie. Porechie, a near-river-area in Russian, is an agricultural landscape where all agricultural land and settlements are situated within a river valley because interfluves are not suitable for agriculture due to natural limitations. The model uses
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a special curvilinear coordinate system, where the position of each settlement is described by two coordinates - along the river and - away from the riverbed, which can be considered as independent random variables. Then the model includes two components: a settlement model along the river and a settlement model away from the riverbed. The mathematical analysis of certain natural assumptions of the model gives us the following results. The distribution of settlements along the river obeys a uniform probability distribution. The distribution of the remoteness of settlements away from the river obeys an exponential distribution. The empirical testing of the model was done and further regularities were obtained for transport-communication tasks like distance distribution between neighboring settlements. The research is done with the help of RGO-RFBR, grant # 17-05- 41141.
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T04 Geomathematics and Marine Geosciences Jan Harff, Di Zhou Marine geosciences are playing an increasing role in studying the Earth’s system and litho-, hydro-, bio- and anthropo-sphere interactions, in mitigating the threats of climate change and natural hazards, and in exploring natural resources and managing the coastal and marine environment. Geomathematics provides essential theory, methods, and techniques used in marine scientific survey, monitoring, and modeling. This topical session aims at presenting new results and progresses worldwide in mathematical applications to marine geosciences. The general scope covers the integration and analysis of multidisciplinary big data, the construction of digital ocean models, the numerical simulation of plate and basin tectonics, atmospheric dynamics and oceanographic processes, statistics, geostatistics, and fractal analysis in marine resource evaluation, costal zone management, and future projection of climate change and related processes. Marine geoscientists, oceanographers, marine biologists, coastal engineers, socio-economists, mathematicians, computer scientists, and pertaining students are invited to attend this session. We would also appreciate the participation of politicians, stakeholders and decision makers in marine management and planning. The presentations and discussions in the session will foster the co-operations between scientists of various disciplines and between scientists and stakeholders. The publication of presented papers is anticipated.
T0401. Mathematical Marine Geosciences: A Time Series Paleo Perspective Manfred Mudelsee Marine sediment cores contain stored information about past climates, which can be unlocked via the measurement of proxy variables. The proxy variables show also influences other than climate. Hence, there is proxy and measurement noise. The shape of the noise distribution is usually nonnormal. The noise process also exhibits autocorrelation. The time information comes from dating, which combines absolute measurements, correlation and makes assumptions about the accumulation of an archive. Hence, there is also timescale noise. Climate time series analysis, based on marine and other archives, has to take fully into account the various noise sources in order to deliver realistic uncertainty measures for the estimations of climate parameters. The mathematical technique of block bootstrap resampling is used to deal with nonormal distributions and autocorrelation. The technique of parametrically modelling the timescale is used to generate simulated timescales and deal with timescale errors. Both techniques have become technically feasible with the advent of the computer age. The validity of 119
T04. Geomathematics and Marine Geosciences these novel statistical tools can be examined by means of Monte Carlo experiments on artificial data. The present paper illustrates the concepts of paleoclimate time series analysis for the two areas of spectrum estimation and trend analysis. It is also a plea for strengthened collaboration networks between statisticians and researchers of past climates.
T0402. Singularity analysis of extreme events occurred along oceanic plate boundaries Qiuming Cheng Several types of geo-events such as heat flow, earthquakes and magmatism occurred along the boundaries of oceanic plate often cause anomalous amount of energy release and mass accumulation confined in narrow temporal or spatial domains. These types of events are termed extreme events in the current paper due to their singular distributions of energy density or mass density cannot be described by dynamic models based on the ordinary integral or differential derivative operations measured in Euclidean space. In this paper new definition of fractal density and new operations of fractal integral or fractal derivative will be introduced and utilized to modeling the anomalous distribution of thermal energy release at the mid ocean ridges during the divergence of oceanic plates, ad mechanical energy release due to earthquakes and volumetric density of magmatism along subduction zones. The strength variance of singularities of these types of extreme events are ascribed by geometric and rheologic properties of oceanic plates.
T0403. „Noise“ – an integral part of climate modelling Hans von Storch, Xueen Chen, Shengquan Tang Traditionally, climate variations are related to some causes. However, some variations may be generated internally Key components of the climate system, in particular the oceans and the atmosphere, are systems associated infinite many nonlinear, often chaotic processes. As a result, the trajectory of the system can be described as an inert system subject to internally generated variations, which may be conceptualized as “noise” . Quasi-realistic models of the oceans, the atmosphere, and coupled systems mimic this stochastic behavior. Without such “noise” , the dynamics of the oceans, the atmosphere and the climate system are incomplete and lack significant features. When models describe macro-turbulence, then the noise generation should take place – in particular in models of the atmosphere, which resolve “weather” . Ocean models, which describe eddies, should do as well. In our paper, we address three aspects – first the general character of noise in ocean, atmosphere and climate models, second an experiment with climatologically forced ocean models on the formation of internal variability, and thirdly the implication for climate analysis Our experimental set-up makes use of a series of ocean models, which are subject only to climatological atmospheric forcing . The first model in the hierarchy is global and is hardly generating eddies; the second model with a grid resolution of 0.4o is embedded into the global model and covers only the West-Pacific. The third with a grid resolution 0f 0.04o is embedded in the West Pacific model and describes
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only the South China Sea. Already the coarsest (global) model such variability is generated, which is becoming larger when we increase of the grid resolution. The presence of “noise”, i.e., of variations unrelated to any external factors, leads to a number of challenges. Two of them related to ”Detection and Attribution” and to ”dynamical downscaling”.
T0404. Enhanced Principal Tensor Analysis (PTA SSA): a tool for multi-way geological data reconstructions Sergey Kotov, Heiko Paelike Principal tensor analysis (PTA) is a modern tool for multi-way datasets reconstructions. A good geological example is paleoclimatic data from deep sediment cores distributed in time, space, and different measured proxies served as climate archives of the past. Direct tensor decomposition applied to geological records usually results in noisy patterns, which are difficult to interpret. We develop an advanced method of PTA: PTA enhanced with Singular Spectrum Analysis (SSA). The method has been applied to 4-way data tensor (time – space – proxies – delaytime) constructed from marine sediment proxies (IODP database). In one computer run, we were able to extract several informative components from the noisy climatic data, including those with main Milankovitch frequencies, removed non-linear trend, located sites and recognised appropriated proxies best suited for paleo-climatic reconstructions. The tool is implemented as an R function.
T0405. Multivariate geostatistical analysis of sedimentological and geochemical facies of a polymetallic nodule accumulation area within the Clarion-Clipperton Facture Zone, equatorial northern Pacific Ocean Łukasz Maciąg, Jan Harff The facies of sediments is regarded a multivariate random field describing the spatial variability of sea bottom morphology, sediment texture and concentration of geochemical elements, in particular heavy metals. As research area serves a claim of Interoceanmetal Joint Organization (IOM). Data have been acquired from sediments sampled by box corers. Lab methods included laser grain size analysis, XRD, XRF, AAS and ICP-MS. A drift analysis shows a trend of most of the facies variables (polynomial degree 2nd and 3rd, trending mostly N-S) so that universal kriging have been applied for the generation of contour maps for the textural, structural mineralogical and geochemical variables determining the multivariate sediment facies in the area of investigation. For multivariate study a model of multivariate classification have been deployed. In a first step facies types have been identified. In the second step Mahalanobis distances have been calculated at each sampling site between the vector of empirical data and the centroids of facies classes determined before. After the transformation into Bayes probabilities of class memberships these probability values are regarded realizations of random variables and kriging has been applied to interpolate the data onto a orthogonal grid. Each grid node has been assigned to one of the classes considered based on the maximum value. The results is to be regarded a thematic map of the occurrence of sediments fa-
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T04. Geomathematics and Marine Geosciences cies classes within the area of investigation. Mapped maximum probabilities give an expression of classification reliability. Prepared models can be usefull for ore prospection, polymetallic nodules genesis considerations and industrial application of silty-clayey pelagic sediments,
T0406. Mathematics for CO2 geological storage Di Zhou The 2015 Paris Agreement emphasized the urgent need in “holding the increase in the global average temperature to well below 2°C above pre-industrial levels” (UNFCCC, 2015). The carbon capture, utilization, and storage (CCUS) is the only technology that can enable a large-scale reduction of carbon emission from fossil fuel usage in a manner compliant with the 2.0°C limit, allowing countries to maintain their energy security. Without CCUS, the cost of mitigation would more than double – rising by an average of 138% (IPCC, 2014). CO2 geological storage is an essential component in the CCUS chain. With enhanced oil recovery by injecting captured CO2 (CO2-EOR), a dual benefit can be obtained. CO2 geological storage opens a new field for geological applications. Mathematics plays an important role throughout all stages of CCUS, from regional estimation of CO2 storage capacity, through site selection and characterization, engineering design, CO2 injection and monitoring, to post-injection monitoring. Mathematical methods of statistics, geostatistics, GIS, and numerical modeling are extensively employed. This paper briefs the functions of mathematics in the stages of CCUS. Special empathies is given to capacity estimation and numerical modeling of CO2 injection, on their tasks and complexities, current status, and several real examples. The mathematical applications for CCUS follow in many aspects the applications in hydrocarbon exploration and exploitation, but modifications are made for the changes in P/T, fluid properties, chemical reactions and so on by the additional CO2 phase, and for the particular concern on storage safty. As a newly developed technology, there are large knowledge gaps in CCUS, and there are much can be done by mathematical geologists.
T0407. Models to display coastline change as paleo- and future scenarios Jan Harff, Andreas Groh, Joanna Dudzinska-Nowak, Andrzej Osadczuk, Ryszard K. Borowka, Hongjun Chen, Jakub Miluch, Peter Feldens, Ping Xiong, Yugen Ni, Wenyan Zhang The contest for dominance of continent and ocean on the geological time scale is determined by the cyclicality of relative sea-level change (rsl) ordered hierarchically by the periods between 50 Ma for the whole Phanerozoic to Milankovitch cycles for the Quaternary. Beyond the 100 ka periodicity determining the Pleistocene paleoenvironment on the continental shelves in particular the 20 ka period can be found as reigning frame for sequence- stratigraphic architecture of the sediment cover on the continental shelves during the Last Glacial Cycle (LGC). The reason for this global rsl-cyclicality is mainly the water volume effect of ice storage during cold periods on the continents and melt water discharge to the ocean basins during the interglacial periods. This global pattern of eustatic change is superposed regionally on the shelves and continental margins by differentiated vertical crustal movement due to changing load of ice, water and sediments as well as tectonic uplift and subsidence. The sea-level equation (SLE) for the prediction of crustal deformation by loading of ice and water is to be combined with empirical data of tectonic crustal 122
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deformation and sediment accumulation in order to reconstruct regional paleogeographic scenarios and future projection of coastal development. The Beibu Gulf with its wide shelf of the South China Sea as a counterpart of the Sunda Shelf can be regarded an ideal area to study the influence of global climate variation, crustal deformation and sediment dynamics on coastal processes. The sea-level equation embedded into a complex methodology is used to describe the formation of marine basins of continental margins. Hindcasts are generated by “backstripping” known from sedimentary basin analysis, whereas “forward modeling” procedures are used for future projections. The general methodology is useful for the generation of strategies in coastal zone management and planning.
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T05 Dimensionality Reduction and Local Methods for Big Spatial and Space-time Data Dionissios Hristopulos, Denis Allard Big data is starting to make an impact in the geosciences given the abundance of remote sensing and earth-based observations related to climate and environmental processes, as well as the existence of large databases pertaining to mining applications. This data explosion underscores the need for algorithms that can handle large spatial and space-time data sets. Most current methods of data analysis, however, have not been designed to cope with large data. Therefore, new methods are needed which can achieve favorable scaling of computational resources with increasing data size. This session will comprise contributions focusing on methodologies that can lead to improved scaling of the computational resources with the size of the data as well as the analysis of interesting big spatial or space-time data sets by means of existing methods. Examples include local approximations, sparse constructions, dimensionality reduction techniques, and parallel algorithms. Contributions using local approximations may involve methods such as maximum composite likelihood and maximum pseudo-likelihood and covariance tapering. Dimensionality reduction contributions will comprise methods such as fixed rank kriging, polynomial chaos, and Karhunen-Loève expansions. Sparse constructions will involve, among other topics, Markov random fields and extensions to irregularly spaced data by means of various approaches. The methodological tools that will be portrayed in this session could have their roots in traditional spatial statistics or in machine learning.
T0501. Non-stationary environmental and meteorological data interpolation using principal component regression Konstantin Krivoruchko, Kevin A. Butler We discuss key features of an empirical Bayesian kriging (EBK) regression methodology which utilizes an informative prior distribution, automatically transforms the dependent variable to a Gaussian distribution, spatially subsets the data to estimate local models and optionally merges the local models. Additionally, for computational efficiency, a chordal distance metric is used when data are collected over large areas. Following the practice of land use regression, we explore the impacts of a diverse set of environmental explanatory variables on the predictions. 125
T05. Dimensionality Reduction and Local Methods for Big Spatial and Space-time Data To mitigate the impacts of potentially highly spatially cross-correlated explanatory variables, we make predictions using principal components where the explanatory variables are orthogonally transformed into a set of uncorrelated components. We illustrate the advantages of the EBK regression methodology using pollution and weather data collected across the contiguous United States. The explanatory variable rasters are derived from widely available geographic data such as roads, industrial facilities, population density, solar insolation, elevation, and wind speed. We show that predictions in areas with fewer observations are more accurate and that prediction standard errors are smaller due to the locally estimated mean value and spatially varying residual semivariogram model. Finally, we show how EBK regression can be used with very large data (millions of points) collected over the entire Earth.
T0502. Unsupervised landform classification using automatic scale selection and Gaussian mixture model capable of distinguishing between different types of lowlands Jaroslaw Jasiewicz, Tomasz Stepinski There is a continuing interest in automatic classification of landforms using DEM data as an input. In a method presented here we adopted our earlier work on geomorphons (Geomorphology 182, 147-156, 2013). In geomorphons an irregular octagon around the focus DEM’s cell formed by the ranges of visibility from the cell in eight major directions was used to extract a digital signature around each cell, match it to a priori template and to produce maps of landforms but without any relief information. In the present work, we extract from that local patch numerical values representing terrain variables, such as an elevation, variation, absolute or relative position of relief, and geometric properties of patch’s shape. Such signature is too complex to match to any finite-size set of templates, but it contains enough information to be classified into a landform types. We classify DEM cells using an unsupervised technique in which emerged landscape classes are given physical interpretation afterwards. We build first a Gaussian mixture model on a sampled data where the number of components in a GMM determined the number of landform types. In the second step we apply the model to each cell of the DEM to obtain the final classification. We apply our method to classify landforms in Poland using the 30m resolution DEM covering the entire country ( 600 mln cells, each containing seven attributes). The data model employed a mixture of 10 Gaussians (landform types). Four types pertain to a high relief terrain - mountains, highlands and its valleys. More interestingly, we obtained six different types pertain to a low relief terrain instead grouping them into single landform. Since most of the terrestrial landmass is belongs to lowlands, a classifier capable to capture a variability of lowland forms opens new possibilities for automated terrain analysis.
T0503. Effective Probability Distributions for Spatially Dependent Processes Anastassia Baxevani, Dionissios Hristopulos Many natural phenomena of environmental and engineering interest very often exhibit asymmetric histograms, heavy tails and complex patterns in space. The latter are in principle incorporated in the joint probability density function (pdf)
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of the underlying random field model. However, the probability distribution of the system cannot be directly evaluated - only few joint pdf models are available in an explicit form - while numerical evaluation is also computationally prohibitive due to the very large size of the problem involved. The most commonly used methods are based on non-linear transformations of Gaussian models. However such approaches are limited in scope, because they only operate on the marginal distribution of the data. Additionally, there is the computational problem of manipulating (storing and inverting) big covariance or inverse covariance matrices. We propose an alternative approach based on an effective pdf that replaces the joint pdf by a product of marginal pdf’s with spatially dependent parameters. Basically, the ”many-body” problem is transformed into a suitable single-body problem. In our perspective, the impact of spatial variation is introduced by means of spatially dependent model parameters and the local dependence of marginal probabilities. The motivation is that either nonparametric estimation techniques can be employed to obtain the parameter estimates or flexible types of spatial dependence can be obtained using Gaussian random fields as models for the parameters. We illustrate the proposed effective pdf approach by modeling synthetic data, including simulations of asymmetric Tukey g-h random fields.
T0504. Introduction to a Stochastic Local Interaction Model and Applications Dionissios Hristopulos, Andreas Pavlidis, Vasiliki Agou, Giota Gkafa In this study, we present a new, computationally efficient method for spatial prediction based on the stochastic local interaction (SLI) model. SLI constructs a spatial interaction matrix (Precision matrix) that accounts for the data locations and sampling density variations. We use a simplified SLI model based on (Hristopulos, 2015). The SLI parameters are estimated through minimization (maximization) of a chosen cost function, such as the Root Mean Square Error. SLI does not require matrix inversion for parameter estimation and spatial prediction, thus reducing the computational time. A precision matrix built with compact kernel functions (Spherical, Quadratic, Triangular, etc.) allows the use of sparse methods, which significantly improves computational time and memory requirements compared to classical geostatistical methods. To illustrate the SLI method, a coal thickness dataset based on approximately 11,500 drill-holes from Campbell County (Wyoming, USA) is used to generate an interpolated thickness map. This data set is chosen because of its economic significance and geological heterogeneity that complicate the estimation. SLI uncertainty measures for the predicted coal thickness values (analogous to kriging standard deviation) are estimated. We compare SLI with ordinary kriging (OK) in terms of estimation performance using cross validation analysis and computational time requirements. SLI cross-validation results based on different kernel functions are presented. The SLI and OK leave-one-out cross-validation measures are comparable, but SLI benefits computationally from a sparse precision matrix structure. References: D. T. Hristopulos. Stochastic Local Interaction (SLI) model: Bridging Machine Learning and Geostatistics. Computers and Geosciences, 85(Part B):26–37, December 2015. A. Pavlides, D. T. Hristopulos, and R. Olea. Estimation of coal reserves: Comparison of the stochastic local interaction model and ordinary kriging with an application to a coal deposit in Wyoming, USA. In 35th IGC, Cape Town, South Africa, Sept. 2016. Paper 1796, Accessed Jan. 11, 2017
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T06 Developments in Methods and Software Tools for Assessment of Non-Renewable Resources Mark J. Mihalasky, Carlos Roberto de Souza Filho, Emmanuel John Carranza, Vesa Nykänen, Kalevi Rasilainen The ability to rapidly and accurately integrate and synthesize geoscientific information in an unbiased, judicious manner is fundamental to supporting and facilitating decision-making in an increasingly data-rich world. Land managers, policymakers, and legislators are making greater use of non-renewable resource assessments to identify and evaluate compatibilities and conflicts among competing resource development and conservation interests. Over the last three decades, particularly with the routine use of personal computers and the advent of geographical information system and other geostatistical software, the development and application of new and innovative methods for assessment of non-renewable resources has increased dramatically. By contrast, packaging of these approaches and techniques into workflows and reliable, user-friendly, publically available/open-source software tools and utilities to facilitate resource assessment has not been as swift. This thematic session explores recent and on-going efforts in the development of quantitative and qualitative non-renewable resource assessment methodologies and tools. It is organized into three sub-sessions that address: (1) theory and methods, (2) implementation of methodologies in the form of workflows and software tools, and (3) the practical application of methodologies and tools as highlighted by case studies. Each sub-session concludes with an expert panel / presenter question and answer period.
T0601. Big data-based mapping mineral prospectivity Renguang Zuo The ideas of big data which include that (1) that they are based on full samples rather than partial samples; (2) that they do not take into account causal relations, but statistical correlations among all the available data; and (3) they are based on data science and let the data speak for themselves, can be used for mapping the locations of mineralization because the core function of big data is prediction. In this study, big data ideas were used for mapping Fe polymetallic mineralization in southwest part of Fujian Province of China. A total number of 43 variables 129
T06. Developments in Methods and Software Tools for Assessment of Non-Renewable Resources which include 2 geological, 2 geophysical, and 39 geochemical GIS layers was used for detecting anomalies related to mineralization using machine learning method. The results show the detected anomalies have a strong spatial relationship with the locations of known mineral deposits. These indicate that the big data is a powerful tool for fusion of multi-sources geoscience data and for making a spatial decision in supported by machine learning methods.
T0602. Using space-time cubes for visualization, exploratory data analysis, in-depth data analysis, and to inform policy Joshua Coyan Spatiotemporal datasets are challenging to visualize and analyze without adequate tools. Displaying data as a three-dimensional space-time cube (STC) with spatial data plotted on the x- and y-axes and temporal data plotted on the z-axis gives researchers an effective visual tool for data interrogation. Powerful, modern computer-processing allows researchers to render, rotate, pan, zoom, adjust transparency, and query spatiotemporal data easily in real time. Additionally, various statistical analyses can be applied to STC datasets (i.e., Trend, Hot Spot Analysis (HSA), Emerging Hot Spot Analysis (EHSA), and Cluster and Outlier Analysis (COA)). Trend analysis is a non-parametric, rank correlation assessment to determine monotonically increasing or decreasing trends through time. The HSA and EHSA detects statistically significant positive or negative clusters and then, in the case of the EHSA, groups the data into 16 predefined categories. The COA distinguishes between negative-spatial correlation (perfectly dispersed), positive-spatial correlation (perfectly clustered), and no spatial correlation (randomly dispersed). To distinguish between a statistically significant trend and a product of random chance, a z-score and accompanying probability (p value) are calculated. For activities involving future prediction, the Trend and EHSA analyses are recommended; these analyses identify areas with consistent past activity and summarize the patterns in terms of warming (increasing activity) or cooling (decreasing activity). COA is ideal for determining the location of high or low clusters or areas with outliers. By combining STC visualization and associated statistical analyses, these tools stand to be valuable for evaluating spatiotemporal data for resource assessment, mineral potential mapping, mineral exploration, land-use planning, or tract delineation.
T0603. Multi-Point Statistics for Tailings Deposits Sangga Rima Roman Selia, Raimon Tolosana-Delgado, K. Gerald van den Boogaart, Helmut Schaeben Technical and economic evolution of the mineral industry resulted in a new view of mining tailings. Formerly tailings are considered not valuable but now they become new resources that have promising economic values. The spatial estimation of mineral distribution is essential for optimally exploiting tailings, but this faces several issues such as non-stationarities, complex and artificial structures, and limited historic information on the feed streams and spilling points. Multi-Point Statistics methods are capable of reproducing complicated structures more appropriately as compared to two-point statistics methods. This paper proposes a new framework for performing Multi-Point Statistics on tailings deposits. Instead of using one big training image, we used several training images. In this way we can use different
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joint distributions at different locations to cope with the nonstationarity of tailings deposits. By providing and eventually weighting training images generated with different forward modelling parameters we can handle the uncertainty about the history of the deposit, while still exploiting available historic information. The framework is illustrated through a test on a synthetic tailings model. The synthetic truth and the training images are generated using Delft3D-Flow, an open source process-based modelling program that can also perform stratigraphic forward modeling in deltaic depositional environments. The MPS analysis is based on a new implementation with advanced capabilities.
T0604. Spatial analysis of mineral deposit distribution: examples in the Carajás Mineral Province, Brazilian Amazon Carlos Roberto de Souza Filho, Paulo Haddad-Martim, Emmanuel John Carranza The formation of ore minerals in hydrothermal deposits is the result of a complex interplay between physical and chemical processes that are conditioned by the geological environment where they occur. In the last decades, research has increasingly indicated that many of these processes display different forms of scale invariance, i.e., they show fractal geometry. This characteristic suggests that behind the apparent disorder and irregularity of the geometry of mineral deposits at different scales, an underlying regular pattern is present. If properly understood, this regular geometrical pattern could be useful in a variety of theoretical and applied fields. A great portion of this scale invariance is given by the structural framework during mineralization, because structures are a dominant factor controlling fluid flow. Here, we assess the geometry of iron oxide-copper-gold (IOCG) mineralization in the worldly known Carajás Mineral Province, focusing in one of the largest and most economically important mineralization, the Sossego deposit. The geometry of mineralization is evaluated at the micro-scale (ore minerals in thin sections), localscale (orebodies in mine maps) and regional-scale (deposits distribution in regional maps). We show that the spatial distribution and shape of ore minerals in the micro-scale is largely non-random, presents fractal geometry and displays defined trends in spatial distribution and anisotropy. Additionally, the geometric trends observed at the micro-scale mimics those of the local-scale geometry of orebodies, as well as the regional-scale distribution of mineralization. The main property controlling the observed scale invariance is permeability, which is intrinsically associated with multiple scale structures. These results contribute to further understanding the fractal nature of processes controlling mineral deposits formation, also revealing new multi-scale approaches to investigate the structural controls on ore deposition and novel strategies to produce mineral potential maps.
T0605. An empirical approach for defining continuous fuzzy memberships for prospectivity modelling of Central Lapland Greenstone Belt Johanna Torppa, Vesa Nykänen, Ferenc Molnár In 2D mineral prospectivity modelling, data sets complementary to each other are integrated to generate a single map describing the probability of occurrence for a certain type of mineral deposit. Regional prospectivity maps are used to guide further exploration in prospective areas where it is worthwhile to gather data for
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T06. Developments in Methods and Software Tools for Assessment of Non-Renewable Resources more detailed information in 3D. One of the most commonly used data integration approaches uses fuzzy logic where, prior to integration, the data are transformed to fuzzy membership values that describe how each data set alone responds to the probability of finding mineral occurrences. In an ideal case, the transformation would be purely knowledge-driven, in which case a perfect conceptual model is used to describe the dependence of the measured quantities on the mineral occurrences and their environments. However, since our conceptual models are yet often insufficient for accurately describing complex regional geology in terms of the measured quantities, data on known occurrences should be used to guide the transformation if possible. We present a way of using the fuzzy approach in a semi data-driven form. Our approach preserves the continuity of originally continuous data by fitting for each data set a Gaussian or a logistic transformation function to the empirically defined histogram of the probability of mineral occurrence discovery. We use known deposits in the study area to define the probability histograms, but also another geologically similar area can be used as a training site. The approach is considered semi data-driven, since the user can constrain the transformation function parameters in the case of noisy data. We apply the method to geophysical and geological data from the Central Lapland Greenstone Belt to generate a prospectivity map for orogenic gold deposits.
T0606. A 3D subsurface model of the Erzgebirge for 3D mineral potential mapping of Sn-W deposits with artificial neural networks Andreas Brosig, Andreas Knobloch, Claus Legler, Peggy Hielscher, Sven Heico Etzold, Enrico Kallmeier, Peter Bock, Andreas Barth Since the 12th century, the Erzgebirge has been an important center of metal mineral mining, especially for Ag, Fe, Cu, Sn, W and later U. Because of the long mining history and the large amount of geological, geochemical, geophysical and mineral data, the Erzgebirge was selected as the test case for developing advanced mineral predictive mapping approaches. The developed 3D model covers an area of 9500 sqkm to a depth of 3000 m below sea level, providing an excellent framework for predictive mapping of minerals mineable in the near future. It focusses on the ore controlling litho-stratigraphic and tectonic framework with detailed consideration of intrusives and the close integration of known Sn and W occurrences. Geological primary (bore holes) and derived (maps, sections) datasets, as well as geophysical and geochemical data were used for geological modeling. Hidden granite intrusions were constrained by 3D inverse gravimetry modeling. Enveloping bodies of known Sn-W occurrences were modeled using data either from literature or provided by exploration companies. They are classified according to commodity content and genetic type for later use as training data in the neural network. Secondly, a software utilizing voxel datasets for artificial neural network based predictive mapping (advangeo® 3D Prediction Software) was developed. The 3D model software was used in generating a voxel-based Sn-W predictive model for the Central Saxonian Lineament with its underlying hidden granite intrusive. Key to predictive modelling is the creation of separate models according to the genetic types of deposits (e.g. Sn in skarns or pneumatolytic veins) to fully account for the different geological factors controlling different types of ore genesis. Field reconnaissance led to the discovery of Sn-W-mineralisations in predicted areas. The model is the starting point for new discoveries supporting the Saxon mineral exploration sector and the development of advanced mineral predictive mapping technologies. 132
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T0607. An emergent self-organizing map and compositional data analysis approach to predicting rare earth element potential in hydrocarbon produced waters of the United States Mark A. Engle, Charles W. Nye, Ghanashyam Neupane, Scott A. Quillinan, J. Fred McLaughlin, Travis McLing, Josep A. Martín-Fernández A domestic source of rare earth elements (REE) is an advantage sought by many countries, including the United States. Produced waters generated during hydrocarbon withdrawal often contain elevated mineral concentrations, but their REE content is largely unknown. Collaboration between the University of Wyoming, Idaho National Laboratory, and the U.S. Geological Survey (USGS) allowed for collection and REE analysis for >100 produced and geothermal produced samples from across the United States. This dataset of REE concentrations and nearly 20 other parameters and compounds were converted to log-ratios, to account for the compositional nature of the variables, and used as input to train an emergent-self organizing map (ESOM). The ESOM organizes data based on their multidimensional structure and differs from conventional self-organizing maps in that it contains more nodes than data points and is able to identify more complex structures. Each new data point that lacks REE concentrations is mapped to a node on the trained ESOM with the smallest Aitchison distance between the node and the data point. The values for REEs are then taken from the codebook vector for the mapped node, and back-transformed into the original units, creating estimated concentrations. This estimation method has the advantage that data with missing parameters can still be used for prediction and that different subpopulations can be left in the same model (as opposed to regression methods). Cross-validation using a subset of the original data indicates that REE concentrations are predicted within one order of magnitude or better. Following this same approach, REE concentrations of produced water across the country were estimated by mapping the contents of the USGS Produced Waters Geochemical Database to the trained ESOM. Initial predictions and associated maps suggest that in some basins individual REE may exceed their concentration in seawater by more than 100 times.
T0610. Mineral potential mapping and resource estimation with artificial neural networks using the advangeo® Prediction Software: Background, case studies and experiences Andreas Knobloch, Andreas Barth, Andreas Brosig, Thomas Kuhn, Daniel Boamah, Kwame Boamah, Henrik Kaufmann Artificial neural networks (ANN) are a powerful data-driven modelling approach for the creation of mineral potential maps. Based on a “self-learning” process, the technology can interpret almost any geo-scientific data to generate both qualitative (prediction of locations) and quantitative (prediction of locations, grades, tonnages) predictive maps. ANNs are learning by analysing footprints of known mineralisations in a framework of suitable geo-scientific data and help to identify controlling features. Highquality geological maps are the source of information for lithologies, ages, structures, tectonic and magmatic events. During data preparation, this information is “translated” into a format, which can be understood by the ANN. The “translation” tools 133
T06. Developments in Methods and Software Tools for Assessment of Non-Renewable Resources are classification of linear features according to their size, type and direction, as well as classification of rocks according to their chemical and physical properties, ages and connection to tectonic events. “Halos of influence” are created around the features to consider the spatial uncertainty of data and reflect distances to important minerogenic factors. Gridded/raster data such as geochemical and geophysical fields and elevation models need separate processing by creation of derivatives like slope, aspect and curvature. Further, they can be evaluated in the context of the geological data to analyse angles and directions. The introduction of a knowledge component allows creating hybrid models that are able to consider known dependencies and regularities. This approach increases the sharpness of the prediction accuracy considerably. Calculation results can be verified by different technologies, including error curves, distribution analysis, cross validation and field verification. The influence and importance of the different model input data can be analysed by several methods that calculate the sum of the weights that originate from a specific data layer. This contribution describes through several case studies how the ANN technology can be applied successfully for qualitative and quantitative mineral predictive mapping in 2D.
T0611. New ArcSDM5 toolbox used for orogenic gold prospectivity modeling within Northern Fennoscandian Shield, Finland Vesa Nykänen, Maarit Middleton, Tero Niiranen, Tero Rönkkö, Janne Kallunki, Juha Strengell, Kimmo Korhonen Enhanced time-saving and cost-effective data-analysis techniques are needed due to increasing amount of digital data collected during mineral exploration. Mineral prospectivity modeling (MPM) can be defined as a multi-step process of extracting and weighting mappable features indicating the critical parameters of the mineral deposit types, or more preferably the mineral systems, that are being sought. The aim is to delineate the most favorable areas for the deposit type in question by using the data-analysis power of modern geographical information systems (GIS). The MPM techniques can be divided into data driven or knowledge driven according to the approach. Data-driven techniques can be either supervised or unsupervised, depending on whether prior knowledge of the modeled deposit type is used. A knowledge-driven approach translates expert understanding of the exploration criteria into a mathematical formula that is supposed to mimic the decision-making process of an exploration team. The project called Mineral Prospectivity Modeler investigated and implemented new workflows and tools for MPM using GIS. In addition to the previously existing traditional tools like weights of evidence, logistic regression, fuzzy logic and neural networks, a collection of new tools were also implemented in an experimental toolbox. These new tools include Adaboost, BrownBoost, Random Forest, Support Vector Machine and Self Organizing Maps. In addition a set of model validation tools were introduced. The code for ArcGIS and ArcGIS Pro is available from the project GITHub site https://github.com/gtkfi/ArcSDM. The use of the tools from reconnaissance to target scale exploration stages was demonstrated in a case study with real exploration data to conduct prospectivity modeling for orogenic gold deposit type within Northern Fennoscandian Shield, Finland. The modeling results were validated using receiver operating characteristic (ROC) curve analysis.
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T0612. U.S. Geological Survey Advancements in Mineral Resource Assessment Methods, Workflows, and Software Tools Mark J. Mihalasky The U.S. Geological Survey (USGS) has a strong commitment to, and a long history of, mineral resource assessment science. Current research priorities include updating and refining 3-Part quantitative mineral resource assessment (QMRA) workflows and tools, and integration of mineral potential modeling/prospectivity mapping (MPM) methods with 3-Part QMRA. The Economic MINeral Resource Simulator (EMINERS) Monte Carlo simulation tool, used to combine probabilistic estimates of undiscovered mineral deposits with models of mineral deposit grade and tonnage to estimate undiscovered mineral resources, has been superseded by Mineral Assessment Program Mark4 (MAPMark4), which is implemented in R and has a user-friendly GUI. Improvements include the application of compositional data analysis and a kernel density method to generate pdfs for ore tonnage and grade, a negative binomial distribution pmf for the number of undiscovered deposits, and estimation of undiscovered mineral resources from only a contained-commodity tonnage model. Output can be further refined with cost model- and Pareto Principle-based economic filter tools. To assist with expert-based estimation of undiscovered deposits, geospatial tools and approaches have been developed that visualize randomly distributed, lognormally-sized deposits in an area being assessed, and portrayal of mineral exploration history in terms of space-time relationships. Other geospatial utilities include a tool to build mineral resource assessment permissive tracts in an automated, easily-updateable, and reproducible manner, and an approach to generate multi-level permissive tracts from MPM that facilitates the estimation of numbers of undiscovered deposits. In addition, for those deposit types that are not easily assessed using 3-Part QMRA, such as stratiform types, a geostatistical simulation technique, based on a multivariate Gaussian random function model, has been investigated and applied to probabilistic estimation of undiscovered resources. The USGS is continuing research and development on assessment methods, workflows, and tools, and is actively engaged with similar efforts with other geoscience agencies worldwide.
T0613. Integrating Mineral Prospectivity Modelling into the Three-Part Assessment Method: The MAP Software Kalevi Rasilainen Geological Survey of Finland (GTK) has carried out national assessments of undiscovered mineral resources since 2008, using the three-part quantitative method and software tools developed by the U.S. Geological Survey (USGS). The three-part assessment framework is considered sound, but there are features missing from the present process and software that would increase the usability of the results. These include the ability to further classify the permissive areas based on their exploration potential, the ability to estimate the proportion of undiscovered resources that could be economically recoverable, and the ability to diminish the uncertainty produced by expert estimates in various phases of the process. The ongoing EIT Raw Materials funded project “Mineral Resource Assessment Platform” (MAP) aims to address these issues. During 2018-2020, mineral prospectivity modelling tools will be integrated into the three-part method, to delineate and further classify the permissive 135
T06. Developments in Methods and Software Tools for Assessment of Non-Renewable Resources areas according to their favourability/potential to contain deposits, and to estimate the number of undiscovered deposits within the permissive areas. Economic filters will be included in the MAP software to provide an estimate of the amount of economically recoverable undiscovered resources. The MAP software will combine code from USGS MapMark4 software and GTK ArcSDM 5 software with new tools developed in the project. The software will cover the whole assessment workflow from the preparation and validation of deposit models to reporting of the results, and its modular structure will enable flexible use of proper tools for each task and easy implementation of new features. The open source software will be freely available. The users are expected to range from government survey organisations to consulting companies, which can use the software to produce services for customers within various sectors, including land use planning and mineral resource exploration.
T0614. A free software for pore-scale modelling: finite-difference method Stokes solver (FDMSS) for 3D pore geometries Kirill Gerke, Roman V. Valisyev, Siarhei Khirevich, Daniel Collins, Marina V. Karsanina, Timofey O. Sizonenko, Dmitry V. Korost, Sébastien Lamontagne, Dirk Mallants In this contribution we introduce a novel free software which solves the Stokes equation to obtain velocity fields for low Reynolds-number flows within 3D pore geometries obtained using, for example, X-ray microtomography. Based on explicit convergence studies, validation on sphere packings with analytically known permeabilities, and comparison against lattice-Boltzmann and other published FDM studies, we conclude that FDMSS provides a computationally efficient and accurate basis for single-phase pore-scale flow simulations. By implementing an efficient parallelization and code optimization scheme, permeability inferences can now be made from 3D images of up to 10e9 voxels using modern desktop computers. The software consists of two parts: 1) a pre and post-processing graphical interface, and 2) a solver. The latter is efficiently parallelized to use any number of available cores To illustrate the software’s applicability for numerous problems in Earth Sciences, a number of case studies have been developed: 1) identifying the representative elementary volume for permeability determination within a sandstone, carbonate and artifical ceramic samples, 2) derivation of permeability/hydraulic conductivity values for rock and soil samples and comparing those with experimentally obtained values. The up to date software, its maual and the link to a escribing journal publication can be found at www.porenetwork.com/download/fdmss/.
T0615. Development and application of petroleum resources assessment system based on network environment and database Mi Shiyun In order to rapidly and accurately evaluate the potential and distribution of worldwide conventional and unconventional oil and gas resources and promote the efficient development of overseas upstream operations, PetroChina has developed a new petroleum resources assessment system based on the network environment and databases with the support of the national special program for petroleum development. Running completely in the network environment and composed of basic
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project management platforms and databases, conventional and unconventional resources assessment software suitable for different objects on different exploration levels, digital mapping and management subsystems, this system achieves many technical targets in developing network-based applications: (1) On a multi-user server, the system resources generated are distributed and coordinated in a rational way; (2) Multiple users send high-intensity computing tasks to the same server from their terminals at the same time, and the tasks will be dealt with in a parallel way; (3) The different steps and intermediate results from different users using the same software will be temporarily saved in the same server, and won’t be overwritten or mixed. Based on the solid geological study, using this system, PetroChina has successfully completed the conventional resources assessment in 425 major oil and gas basins, 607 hydrocarbon-bearing systems and 778 plays, unconventional technologically recoverable resources in 476 oil and gas-bearing systems according to 7 mineral in overseas countries. This lays a solid foundation in advance selection of exploration areas and rapid evaluation exploration projects.
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T06. Developments in Methods and Software Tools for Assessment of Non-Renewable Resources
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T07 Applied Geoinformatics for Mineral Exploration Mana Rahimi, Vesa Nykänen Geoinformatics includes variety of methods and techniques to promote collaboration between the computer science and the geosciences. Multi-source datasets and innovative techniques are the basis of advanced Exploration which improves the rate of successful exploration within reasonable time and budget. Geoinformatics helps the mineral explorers to process, interpret and integrate the enormous datasets through use of advanced information technology. The session aims to provide a forum for the exchange of innovative ideas, approaches, methods, techniques and case studies in the field of applied geoinformatics for mineral exploration. The focus would be on new advances in methodology applied to modern exploration as well as significant case studies, includes but not limited to: Application of Geospatial Information Systems (GIS) in Mineral Exploration Geological Remote Sensing (GRS) Geo-Exploration datasets Geo-Exploration Database Management Mineral Prospectivity Mapping (MPM) Geo-Mapping and Alteration Mapping techniques Application of Geo-simulation Modeling in Exploration Application of Spatial Data Mining and Big datasets in Resource Discovery Geospatial Data Models (GDM) Applied to Geo-exploration Datasets Application of Multi Criteria Decision Making (MCDM) in Exploration Models Multi-dimensional Exploration Techniques Collaborative Geoinformatics for Restraining Environmental Impact of Resource Discovery. Geo-Visualization in Mineral Exploration The emphasis is to bring up innovations and novel applications of geoinformatics, relevant tools and topics in mineral exploration to increase the rate of success in new modern exploration methods and solve complex scientific problems.
T0701. 3D structure modeling and fractal analysis for exploration targeting and mineral resources assessment in Luanchuan polymetallic district Gongwen Wang The 3D GIS technology is an important tool for the deep targeting (de kemp, 2011). The complicated geometries and features of the polymetallic deposits and the geological setting can be constructed by 3D modeling using multiple geosciences datasets including cross-section, geological map, borehole, geochemical assay, and geophysics data. In this paper, the geological setting, the metallogenic model of deposits, and mineral exploration model are correlated analyzed and constructed in 3D space for polymetallic resources and reserves assessment. The methodology and datasets are summarized as follows: (1) the fault-fold-intrusion structure evolution 139
T07. Applied Geoinformatics for Mineral Exploration of geological setting based on the region/district- scale geological and geophysical datasets additional geochronology of intrusion datasets in the study area; (2) the metallogenic system analysis of polymetallic deposits based on deposit-scale explroaiton and mining datasets of three large Mo-W deposits and three large Pb-Zn deposits in the study area; (3) the exploration criteria extraction combing districtscale thrust- fold –intrusion components and deposit-scale mineral system datasets including granite (porphyry) series, mineral types and components (chalcopyrite and pyrite features), C-P-T features of fluid inclusion, and K2O/Na2O in different intrusion zones. The research results show: (1) the thrust-fold –intrusion compounds can be used to identify magma-skarn mineralization center, (2) the 830Ma gabbro and syenite can be used to identify the polymtallic mineralization zones, (3) the 1600Ma Longwangzhuang intrusion is hard constrain for the regional structure, it can be used to indicate the crust-scale structure for the intrusion upwelling and fluid channel, (4) two metallogenic system are identified, and one potential mineral system are extracted, and four secondary mineralizatin centers were identified in the study area. The depth of the magma-skarn type Mo –W deposit is 4km, the depth of the vein-type Pb-Zn deposit is 2km, and the skarn Zn deposit is 3km.
T0702. Objectively grading geophysical interpretations and modelling mineral system uncertainties to generate robust exploration targets Joel N Burkin, Mark D Lindsay, Sandra A Occhipinti, Eun-Jung Holden, David Nathan Prospectivity analyses are used to reduce the exploration search space for soughtafter mineral systems. The scale of a study and the type of mineral system sought controls the evidence layers used as proxies representing critical ore genesis processes in fuzzy-logic knowledge-driven approaches. Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the mineral system model. In the case study presented, the prospectivity of remobilised Ni-Cu in the west Kimberley, Western Australia, is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios. Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered. The first technique grades the strength of structural interpretations on an individual line-segment basis. Interpretations are graded against the objective measure of feature evidence, which is the quantification of specific geometric patterns in geophysical data underlying the interpreted structures. Individual structures are weighted in the prospectivity model with values correlated to their feature evidence. This technique allows interpreted features to contribute prospectivity proportional to their feature evidence and indicates the level of associated stochastic uncertainty. The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems. In this approach, multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition. With a suite of prospectivity maps, the most robust exploration targets are the locations with the highest prospectivity value range amongst the model suite. This new technique offers an approach similar to Monte Carlo simulation as it forces the modeller to consider a range of alternative mineralisation scenarios.
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IAMG2018 - Short Abstracts
T0705
T0703. Hierarchical approach to regional geological modelling Andrei Sidorov jn, Andrei Sidorov, Andrei Plavnik An approach to the creation of detailed geological models for large territories, characterized by vast volumes of heterogeneous and disjointed initial data, is presented. The approach was implemented using the GST (Geo-Spline Technology) software and was successfully applied in detailed structural model of the sedimentary cover across the territory of the West Siberia. The mathematical basis of the approach is a variational gridding method that significantly expands options for usage of initial data. The method also allows the physical nature of the required parameter to be introduced into the model in the form of equations of mathematical physics. The idea of this approach is to present geological information, both initial data and result data, in the form of formal digital objects, each of which is characterized by a personal calculation method. Through the calculation method, objects are divided into two classes: source objects and those constructed based on other objects. Thus, they form a hierarchical structure representing the solution tree. At the root of the tree is initial data, and the higher-ranking elements can be both results of calculations and arguments for constructing elements of a higher rank. This ensures: the creation of a protocol procedure for solving a complex and voluminous geological problem; full compliance of all initial information with the results of calculations; automatic updating of an arbitrarily complex model when changing the raw data. All these aspects are especially important when constructing models for large areas. The gridding method used allows to create grids, taking into account physical regularities in the distribution of geological parameters, which is important when the density of the initial data is insufficient. The approach ensures absolutely strict accounting of all initial information, and, in particular, allows merging regional models and detailed geological models built on local areas.
T0704. Spatial distribution characteristics and mineral prospectivity mapping for tungsten polymetallic deposits in the Nanling region, China Tongfei Li, Qinglin Xia, Mengyang Zhao, Shuai Leng Influenced by the subduction of the Pacific plate since the Mesozoic, the Nanling region experienced the lithosphere thinning and anataxis of tungsten-rich crust which resulted in the explosive occurrence of a large number of tungsten hydrothermal deposits related to granite. Since the mineralization process is a self-organized critical process with the property of chaos, the distribution of mineralization has its randomness and regularity. However, as a kind of consolidated geological body, the distribution of mineral deposits can be regarded as a kind of random process or chaotic process which can be studied by spatial statistic methods. Moreover, the rarity of singular events may lead to data imbalance when mapping mineral prospectivity by machine learning (ML). In this paper, the spatial statistic methods are used to quantify the distribution of tungsten deposits in the Nanling region. The under sampling and SMOTE methods are used to generate the training data to reduce the data imbalance. Then, random forest (RF) and information integration model based on multiplicative process are applied to mapping the prospectivity of tungsten deposits. The results show that: (1) the distribution of tungsten deposits is controlled by the NE and EW trend structures at a large scale but dominated by multi-direction structures at a local scale; (2) the tungsten deposits cluster in 141
T07. Applied Geoinformatics for Mineral Exploration region but distribute randomly in permissive areas which may suggest that different mathematical models should be applied to quantify the distribution of mineral deposits considering the different exploration degrees; (3) the information integration model based on the multiplicative process can provide a singular measurement of the importance of predictors; (4) the prediction result of RF based on the SMOTE sampling is better than that of under sampling, indicating that the SMOTE sampling method can reduce the data imbalance which extends the application of RF in mineral prospectivity mapping.
T0705. Multifractal modeling in wavelet domain for identifying anomalies caused by deep mineral resources Guoxiong Chen Discovering potential mineral deposits in deep area is becoming a new international trend, but deep ores prospecting faces with many challenges, of which the most crucial is that the geo-anomalies/geo-information caused by deeply buried ore deposits are very weak and heavily mixed due to the fact of large depth. Thus, one of key missions for deep ores prospecting is to develop competent methods for enhancing weak anomalies and extracting multiscale information. In this paper, the Nanling W-Sn polymetallic district from south China is chosen as case study area for exploring the hierarchy of mineral system and the self-similarity of geo-anomaly. Specifically, we focus on building fractal density model and multiplicative cascade model on the frame of wavelet transform and fractal concept. This idea comes from the fact that multiscale decomposition scheme of wavelet transform is a natural tool for scaling analyzing the fractal measure. Accordingly, the wavelet-based singularity mapping technique and fractal-based wavelet multiscale decomposition method are proposed respectively for identifying the weak anomalies caused by deeply buried ore deposits, as well as extracting the multiscale deep information buried in mixed geo-anomaly pattern. Of special interest is that the recognized multiscale anomalies significantly enlighten our knowledge for understanding the hierarchy and coupling of multiscale metallogenic system, therefore assisting in the prediction and exploration of deep mineral resources.
T0706. Application of fractal models to delineate mineralized zones in the Pulang porphyry copper deposit, Yunnan, Southwest China Xiaochen Wang, Qinglin Xia The purpose of this study is to delineate and identify the various mineralized zones and the barren host rock based on surface and subsurface lithogeochemical Cu data in the Pulang porphyry copper deposit located in southwest China utilizing the number–size (N–S) and concentration–volume (C–V) fractal models. The N–S model reveals three mineralized zones characterized by Cu thresholds of 0.325% and 1.334%, with zones 1.334% Cu representing highly mineralized zones. Results obtained by the C–V model depict four geochemical zones defined by Cu thresholds of 0.331%, 1.380% and 1.905%, which represent non-mineralized wall rocks (Cu1.905%). Both the N–S and C–V multifractal models indicate that high grade mineralization is situated in the central and northern parts of the ore deposit. Their results are compared with the alteration models resulted from the 3D geological model using logratio matrix. Thus, the results show that the N–S fractal model of highly mineralized zones is better than the C–V fractal model of highly mineralized zones. However, results obtained by means of the C–V fractal model for moderately and weakly mineralized zones are more accurate than the zones obtained by means of the N–S fractal model.
T0707. Chemical responses to hydraulic fracturing at magmatic hydrothermal transition: insight from numerical modeling Xiangchong Liu, Dehui Zhang Magmatic-hydrothermal ore deposits are important sources of copper, molybdenum, tungsten, tin, and gold. How the ore minerals precipitate from magmatic hydrothermal fluids is essentially important for understanding the ore forming processes. Hydraulic fracturing caused by high-pressure magmatic fluids is one of common physical processes among many magmatic hydrothermal deposits and plays an essential role in creating fractures and focusing mineralized fluids, but it is largely unexplored whether and how this physical process causes precipitation of ore minerals. CO2 is a major buffering agent in mineralized fluids and its solubility in NaCl solutions is strongly related to fluid pressure. In this study, how hydraulic fracturing affects solubility of CO2 in NaCl solutions was investigated using finite elementbased numerical experiments. The coupling of rock deformation and fluid flow was governed by poroelastic constitutive equations, continuity equation, and Darcy’s law. The fluids in the numerical experiments were are aqueous NaCl solutions. The solubility model in Mao et al., (2013, Chem. Geol.) was used to reproduce the CO2 solubility in the fluids. A significant decrease in fluid pressure was identified from the numerical experiments once rock is fractured by high-pressure fluids. The fluid pressure drop caused a decrease of 34.6% in the solubility of CO2 on average. Therefore, our numerical experiments suggest that the fluid pressure drop accompanying a hydraulic fracturing process could break the chemical equilibrium and may cause precipitation of ore minerals whose solubility (i.e. W, Sn, Au) is strongly dependent on pH.
T0708. Characteristics and resource potentials of the source rocks in Cambrian System, eastern Sichuan Basin, China Man Zheng, Man Zheng, Qiulin Guo, Jingdu Yu, Jianhua Wang In 2011, the Anyue gas field, the largest integral marine carbonate gas field of China, was discovered in the central Sichuan Basin. This discovery proved the great resource potential in the Sinian-Lower Paleozoic in the central Sichuan Basin. In contrast, the exploration in deep strata of the eastern Sichuan Basin is challenged by unrecognized characteristics of source rocks and underestimated resource potential. In this paper, we investigated the source rocks in the Cambrian Qiongzhusi Formation in eastern Sichuan Basin, through filed survey and sample analysis, and depending on the geological conditions of this basin. It is found that the source
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T07. Applied Geoinformatics for Mineral Exploration rocks in the Qiongzhusi Formation are composed of oil shale and black shale of shallow-water shelf and deep-water shelf facies, with average formation thickness of about 200 m, and dark mudstone accounting for 20%– 60% of total thickness. The �13C value ranges from -31‰ to -33‰, the Type-I organic matters are dominant, and the TOC is 0.1%– 10.2% (2.1% on average). Moreover, the Ro is 2%–3.5% (2.61% on average), suggesting the source rocks at high-over mature stage. According to the calculation by using the comprehensive basin modeling system Basims 7.0, the source rocks in the Qiongzhusi Formation have a great gas-generating potential, with the gas-generating quantity of 153.2tcm, and the initial gas in place of 7,66.1bcm, showing the huge exploration potential in deep Cambrian strata in eastern Sichuan Basin.
T0709. The predictor modelling for the magmatic type mineral resources based on the comprehensive information: a case in the Dazaohuo area, East Kunlun of northwest China ning cui, Keyan Xiao, Jiannan Liu, Jianping Chen, Zhuosheng Liu The estimation of undiscovered mineral resources is an important research project all over world which we can get the potential value of a mineral field and its long-term use. Because of the forming complexity, the most important thing for the assessment of mineral resources is getting more useful geological information using the advanced tools. It experiences many times orogenic activities with three deep faults zones in Dazaohuo area, which makes favorable conditions for the mineral resources forming. With the discovery of magmatic Cu–Ni sulphide deposit such as the Xiarihamu deposit, it shows that there are a great magmatic type resource potential in this area. This paper analysis the genesis model and build up the prediction model for the magmatic type mineral resources in Dazaohuo base on the combination of multiply datasets containing geological, geophysical, geochemical and remote sensing. Six target areas are identified based on integrated GIS-based prediction model. The technique for this methodology includes: (1) developing the metallogenic model for the magmatic mineral resources type in Dazaohuo area; (2) integrating the mineralization information based on the characteristics of known mineral anomalies with geology, geochemistry and geophysics; (3) analyzing the anomalies complied using multiple spatial datasets and building up the prediction model; (4) targeting prospective areas and assessing potentially undiscovered mineral resources; (5) mineral resources and exploration planning. The procedure is tested for magmatic Cu-Ni sulphide resources in a known mineralized zone in the Dazaohuo area, East Kunlun of northwest of China.
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T08 Tools for Analysis of Non-quantitative and Miscellaneous Data. Its Applications Susanna V. Sirotinskaya Today the main part of researches and publications in the field of mathematical geosciences is intended for processing of quantitative data and implementation of statistical methods at this aim. But a significant body of geological data as well as data of some allied fields is descriptive, and these data are given mainly in form of texts and conventional maps. Non-quantitative data (texts, maps) as well as the combinations of them with quantitative ones (tables, contour maps) may be an important source of information for the solution of principal problems in some fields of geosciences. These problems are prediction, evaluation, identification and classification. For example, the need in solution of such problems on the base of non-quantitative data arises usually at the early stages of geological works when it is necessary to evaluate the favorability of areas for the organization of following expensive works or to identify mineral occurrences with the highest potential among hundreds of them. The non-quantitative data may be used also in prediction of geological and ecological hazards (landslides, earthquake). In previous century, several great groups of methods which are based on the deterministic approach have been developed at this aim.These methods are based on tools of discrete mathematics or are heuristic (pattern recognition methods, cluster analysis methods and others). There are also methods based on some theories of computer sciences, including the mathematical logic (cause-effect analysis methods). Unfortunately, the trend of mathematical geosciences aimed at the processing of non-quantitative data and at the development of deterministic methods had no enough attention in the last years.Therefore the papers covering the current state of this area and tracing its future are well come.
T0801. A Series of Software Systems for Verifying Mining Rights in China Yongzhi Wang, Yongjie Tan A nationwide mining rights verification project is carried out by China Geological Survey to investigate and inspect real mining sites and other related information, and aims to protect owners of all registered mining rights and avoid unexpected issues. Datasets associated with each mining rights are composed of a property 145
T08. Tools for Analysis of Non-quantitative and Miscellaneous Data. Its Applications database, a comparative table, a specific description, an elaborate vector map, a result map composed by dozens of shapefiles and a corresponding output map. This project implemented in 31 provinces is very complex and difficult to be performed in three years and to achieve high quality data. Current study introduces a feasible solution to complete this heavily loaded project. First, five common standards including monthly report, property data acquisition, spatial data acquisition, data quality check and data management are drawn to formulate the rules. Second, a series of software systems are developed based on GIS and database by following the above rules: (1) online monthly report system can supervise and enforce work progress of every province; (2) the property data acquisition system can collect every specific values of each mining and exploration rights; (3) the spatial data acquisition system embedded in AutoCAD can generate spatial features which are further symbolized automatically; (4) the data quality check system can examine properties and data quality of every mining rights automatically; (5) the national data management system can manipulate and analyze finalized data of all mining and exploration rights. More than 1200 organizations and companies had utilized these systems to process data collection and checking of 146,877 mining rights. These automatic systems play a significant role on enhancing work efficiency and reducing work load, which at least 8 million dollars directly saved for data transformation. In addition, high data quality can be assured effectively and more sophisticated daily management are expectable.
T0802. Parametric Assessment of the Quality of Estimation Marek Ogryzek, Ryszard Źróbek, Mateusz Ciski The paper will present a method of verification of the results of estimation using geostatistical methods, for various types of data. Application of the Parametric Assessment of the Quality of Estimation (PAQE), allows to indicate the optimal geostatistical method used for specific type of data, presented in the paper – geographic point data. This goal will be achieved by using the optimization algorithm. Automating the verification process using prediction error parameters, available in the esri ArcGIS software will enable selection of optimal geostatistical method for estimating values in unmeasured points on the basis of values at measuring points, in a short period of time. Geographic point data will be used for analysis, covering the whole globe. An important attribute of the Parametric Assessment of the Quality of Estimation (PAQE) is that the method can be used on various types of data. The parameter weighing system can be used, with any chosen parameter weights; also stochastic and deterministic methods of interpolation can be compared. The disadvantage of this model is the need of a large amount of input data, because it is necessary to perform two models and compare the parameters of prediction deviations from cross-validation and subset validation.
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T09 Stratigraphic Forward Modeling Daniel Tetzlaff, Cedric Griffiths This session is focused on the quantitative numerical forward modeling of geologic processes responsible for generating stratigraphic sequences, including erosion, transport and deposition of clastic sediments, growth and precipitation of carbonates and evaporites due to chemical and biological processes, and syndepositional and postdepositional processes that affect stratigraphic geometry, such as compaction, diagenesis and structural deformation. All depositional environments will be of interest to this session, including human caused alterations to depositional systems and environmental change. Studies involving purely theoretical work as well as applications to specific sedimentary systems or basins will be welcome. Time scales of the studied processes may range from seconds to millions of years and may pertain to the geologic past, present, or involve future predictions. Spatial scales may range from laboratory and outcrop scale, through oil reservoir scale to basin scale. The session will also include studies on the response of sedimentary systems to external forcing and boundary conditions (such as sediment supply, sealevel change, folding and faulting) as opposed to autocyclicity and chaos arising internally (such as avulsion and lobe shifting). The use of deterministic models under uncertainty and applications combining process modeling with geostatistics will be considered as well. Topics such as data assimilation and ensemble programming will also be of interest in the session.
T0901. A Digital Flume Tank Cedric Griffiths Sediment flume tanks have been part of the stratigrapher’s tool kit for over a century. In their basic form they consist of a rectangular tank within which is an inclined surface. The tank may be partially filled with a fluid to create a shoreline. Clastic sediment is introduced in a fluid stream while accommodation is increased or reduced. The stratal patterns thus produced are noted, both while the experiment is under way, and by examining sections through the model volume after the experiment is finished. Various degrees of sophistication are possible including vertical movement of parts of the tank floor, variation of sediment grain size and sorting over time, and changes of fluid input velocity, volume and location. These experiments may take hours to weeks to set up, run, interrogate, and dismantle. However, with computational fluid dynamic based stratigraphic forward modelling software such as Sedsim we have shown that not only can we duplicate physical flume tank results, but we can show that there are several advantages to the digital flume tank approach that suggest that it may be preferable to physical modelling. Such advantages include: speed, where typically a digital model is completed in 147
T09. Stratigraphic Forward Modeling a sixth of the time of the physical model; the ability to run many experiments simultaneously with slight changes to input parameters to test for sensitivity to initial conditions; the ease of set-up and clean-up; and the ability to interrogate the internal stratigraphy post-simulation in many modes (time, grain-size, porosity etc.). We show examples of the application of digital flume tank experiments replicating real-world physical experiments but with sensitivity studies included.
T0902. Assessing the potential of response surfaces to perform risk analysis and data assimilation in stratigraphic forward modeling Veronique Gervais, Didier Granjeon, Patrick Rasolofosaon Data acquired during exploration are usually insufficient to accurately characterize petroleum systems. Stratigraphic forward models can be considered to reproduce sedimentary basin infill. However, these numerical simulators depend on input parameters that are generally difficult to estimate (quantify). The induced uncertainties need thus to be taken into account, with simulation calibration on available data and quantification of the remaining uncertainties for decision making. One of the main limitations to properly handle uncertainties is the time required to perform simulations. A way to overcome this issue consists in considering metamodels that mimic the simulator. These meta-models (or response surfaces) are built from a set of simulations – the training set – and provide fast estimations of the simulator outputs for any values of the input parameters. If these estimations are accurate, that is, close to the true simulated values, the meta-models can be used instead of the simulator for different studies such as risk analysis and calibration. In particular, meta-modeling combined to reduced-basis decomposition provides estimates of outputs varying with the location from a limited number of response surfaces (e.g. distribution of sediment thickness or facies proportion in the basin). We propose here to demonstrate the potential of meta-modeling to handle uncertainties in stratigraphic forward modeling. Two objectives will be considered: risk analysis on spatial outputs to identify probability maps, and model calibration. Sensitivity analyzes based on response surfaces will be conducted to better understand the processes at stake and discard the less influential parameters during calibration. The definition of the training sets used to build the response surfaces will also be addressed. Different approaches will be considered depending on the objective of the study to minimize the number of simulations required to obtain accurate estimations.
T0903. Automated Inverse Stratigraphic Modeling Using Differential Evolution Yanfeng Liu, Taizhong Duan, Wenbiao Zhang, Mingchuan Wang This work deals with the method to estimate the parameters that play a role in process-based modeling to match observational data. Compared with geostatistics reservoir modeling method, process-based modeling method produces a more realistic model, but it performs weakly in conditioning modeled architecture with available observations. Inverse stratigraphic modeling was proposed to solve this issue by an automatic matching workflow which is comprised of a forward simulator, a comparison between modeled result and observational data, and a global optimization algorithm. This matching task can be viewed as an optimization problem that was addressed by the differential evolution algorithm in this paper. 148
IAMG2018 - Short Abstracts
T0905
As the variables of the optimization, the input of the stratigraphic forward simulation is replaced by limited parameters to reduce computation and uncertainty. Sea level curve is replaced by a combination of several sine functions and each sine function is determined by amplitude, phase and period. Space dependent parameters of production coefficient, transport coefficient, initial topography and subsidence rate are replaced by the interpreted curve or surface by limited user-defined points. The target of the workflow is to minimize the distance between the simulated section and observed data. A sedimentary facies succession from section is represented by a string of symbols. Each symbol brings with one or more attributes. A similarity measure is defined between the attributed string based on syntactic pattern-recognition technique. A dynamic programming algorithm is used to calculate the similarity. The optimization algorithm links the forward simulator and the comparison. Besides the measured data, only the minimum and maximum value of each parameter are fed in the workflow. Under the differential evolution strategy, all parameters simultaneously grow better and better to produce perfect simulation. The technique has been validated with a synthetic case, and we also tested it by a field reservoir from Yuanba, Sichuan, China.
T0904. Evaluating the structural control over carbonate platforms developed in syn-rift settings Isabella Masiero, Peter Burgess, Lucy Manifold, Cathy Hollis, Johanne Nergaard Grinde, Rob Gawthorpe, Atle Rotevatn, Isabelle Lecomte Characterization of carbonate platforms developed in syn-rift settings is challenging. Carbonate strata are complex and heterogeneous, and the fault-related subsidence characterizing extensional basins increases this complexity. We have used stratigraphic and seismic forward models to investigate how normal faulting may affect architecture, facies distribution and seismic imaging of carbonate strata. The first step of the research has been the improvement of Carbo-CAT, a preexisting 3D stratigraphic forward model of carbonate systems. Lobyte3D is a new CarboCAT sub-routine for modelling sediment entrainment and down-slope redistribution. A novel approach for modelling wave energy distribution has also been developed. Carbo-CAT has been used to investigate how carbonate accumulation may be controlled by syndepositional normal faulting. Two structural configurations have been tested, the nucleation and development of an isolated fault, and evolution and interaction of two overstepping structures. Detailed sensitivity analysis investigates the relationship between carbonate growth and eustatic oscillations, lateral fault propagation rate, slip rate and foot-wall uplift. We have also explored how fault evolution controls redistribution of carbonate sediment from the platform top area into adjacent basins. Adopting a source-to-sink approach leads to calculation of carbonate production and accumulation budgets, analysis of the flux from platform top source to adjacent slope and basin sink areas, and calculation of deep-water deposition volumes. We show how this mass balance varies under different eustatic and fault control scenarios. The main outcomes of the performed sensitivity analysis, are a series of geological models, representing carbonate platform developed under the different, investigated controls. We populated the resulting models with elastic properties, using a depthdomain convolution modelling, integrating both illumination and resolution effects, to calculate synthetic seismic images. We will compare the developed seismic images with known examples of synrift carbonate platform margins, analysing how the
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T09. Stratigraphic Forward Modeling observed seismic platform architectures may relate to eustatic and tectonic control.
T0905. How to quantify the initial source-rock properties at a basin-scale from a stratigraphic numerical forward model ? Benoit Chauveau, Didier Granjeon, Alina-Berenice Christ The distribution and quality of organic matter in source rocks are key elements for petroleum systems assessment. However, these properties are generally poorly known as they do not represent the main target of the drilling process, involving strong uncertainties in the estimation of the kitchen area. Fortunately, the stratigraphic numerical forward model offers a powerful alternative approach for improving our knowledge on the source-rock characteristics just after its deposition as an organic-rich layer. Stratigraphic forward numerical models are well designed to test different geological scenarii and to assess possible depositional environments for organic matter. Recent developments in DionisosFlow allow to explicitly simulate the main processes that control the final organic matter distribution (bulk initial TOC) and quality (bulk initial HI). Moreover, the associated depositional environments are now not only described in terms of bathymetry or sedimentation rate, but also in terms of redox and preservation conditions (in the water column and at the seafloor interface). This approach has already been tested on ancient case studies in very different sedimentary basin types (foreland basins, carbonate platforms, passive margins, lacustrine systems), and on the Congo Deep-Sea Fan over the last 200 kyrs in which both terrestrial and marine organic matters were simulated. All these simulation results demonstrate the capacity of the model to reproduce correct predictions on organic-rich facies properties. If the organic matter model in DionisosFlow opens the way to challenge different conceptual geological scenarii, the more important added value is their use for petroleum basin models. Recent developments in basin models allow the construction of different degradation kinetics from a mixture of kerogens. The conditions of organic-matter preservation (represented by continuous properties) and the relative proportion of marine and terrestrial organic matters are used to evaluate the contribution of each kerogen in the calculation of the effective degradation kinetics.
T0906. Sediment load and transport estimation using random walk theory Daniel Tetzlaff Sediment load and sediment transport rates in open channels are fundamental quantities in geologic and engineeering models of sedimentation. Existing formulas for predicting sediment load differ from one another by well over an order of magnitude. Laboratory experiments and field measurements also show a large dispersion for similar flow conditions. Here we introduce a method to calculate sediment load and transport rates based on the random trajectory of particles in turbulent flow coupled with the effect of gravity and viscosity. This approach predicts both bed load and suspended load from flow depth, velocity, and sediment composition. Although calculations require a numerical solution rather than purely analytical formulas, this single theory accounts well for different modes of sediment transport and also yields a good criterion for the onset of sediment movement. A remaining significant source of uncertainty 150
is the vertical distribution of turbulence, requiring further testing and theoretical development. Nevertheless, results are in accordance with laboratory measurements and field observations with an accuracy that is at least as good as that of existing models. The method provides a better causal physical explanation for the predicted quantities than many existing methods, and requires the application of a single algorithm for a broad range of sediment types and transport modes, making it ideal for use in sedimentation models that require fast computation.
T0907. The weighted curvature minimization: a correction to thickness variations in implicit structural modeling Julien Renaudeau, Frantz Maerten, Emmanuel Malvesin, Guillaume Caumon Structural geological models are commonly constructed by assuming that geological features are as smooth as possible. Implicit methods based on this concept have proven to be efficient in many studies as they can handle sparse, irregular, and noisy data. Unfortunately, smoothing the geometries prevents such methods to represent recurrent geological features such as thickness variations of layers. Controlling the smoothing is therefore necessary yet challenging as it is difficult to perfectly understand its impact on the created models. We suggest to perform a spatial regression of data constraints with a bending energy penalization. The smoothing is controlled by the energy regularization. The specificity of the bending energy is that a physical understanding of its influence on created models is straightforward. We therefore suggest a data driven correction to the thickness variation problem called the Weighted Curvature Minimization, which is based on the bending energy. Our correction applies on 2D cross sections and 3D structural models with faults, folds and unconformities.
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Contributions by topic M010 3D/4D Geomodeling G0101, G0102, G0103, G0104, G0105, G0106, G0107, G0108, G0109, G0201, G0301, P0107, P0117, P0221 M020 Compositional Data Analysis G0001, G0201, G0202, G0204, G0205 M030 Data Assimilation and Data Integration G0301, G0302, G0303, G0304, G0305, G0306, P0221 M040 Data Science G0809, G1007, P0217 M050 Differential Equations and Nonlinear Analysis G0401, P0113, P0210 M060 Fractal and Multi-Fractal Modelling, Singularity Analysis G0401, G0402, G0403, G0404, G0405, P0209, P0218 M070 Functional Data Analysis G0501, G0502, G0503 M080 Geoinformatics G0104, G0601, G0603, G0803, P0104, P0117 M090 Geostatistics – Two Point Geostatistics G0003, G0105, G0106, G0202, G0204, G0711, G0712, P0107, P0115 M100 Geostatistics – Multi Point Geostatistics G0303, G0705, G0709, G0710 M110 Geostatistics – Model-Based Geostatistics G0701 153
M120 Geostatistics – Mining Geostatistics G0109, G0704, G0705, G0708, G0713, P0105, P0109, P0112 M140 Geostatistics – Other Geostatistical Methods G0701, G0702, G0703, G0706, G0714, P0103, P0112 M150 GIS-Based Geochemical Exploration G0403, G0602 M160 Graph Theory G1007 M170 Image Analysis G0805, G0810, G0813, G0815, P0116 M180 Inverse Problem Solving G0107, G0305, G0306, G0901, G0908 M190 Machine Learning, Pattern Recognition, Data Mining, Big Data G0502, G0801, G0802, G0803, G0804, G0806, G0807, G0808, G0809, G0810, G0811, G0812, G0814, P0205 M200 Numerical Modelling and Numerical Simulation G0103, G0503, G0708, G0801, G0805, G0806, G0812, G0902, G0903, G0904, G0906, G0907, G0908, G0909, G0910, G0911, G1003, P0104, P0115, P0208, P0210, P0211, P0213, P0215, P0216 M210 Object Oriented Data Analysis G0712, G0909 M220 Optimisation and Operations Research G0905, P0103 M230 Spherical Data Analysis G1001 M250 Statistics (excluding Geostatistics) – Time Series Analysis G1003 M260 Statistics (excluding Geostatistics) – Multivariate Statistics G1004 M270 Statistics (excluding Geostatistics) – Bayesian Statistics 154
G0808, G1004 M290 Statistics (excluding Geostatistics)– Other Statistical Methods G0815, G1006, G1008, P0212, P0220 M300 Other methods G0002, G0004, G0005, G0006, G0203, G0814, G1002, G1005, P0114 A010 Characterization of Porous Media G0301, G0305, G0805, G0809, G0910, P0107, P0113, P0115, P0210 A020 Climatology, Meteorology, Cryology, Atmosphere and Ocean Sciences G0405, G0502, G0911, G1003 A030 CO2 Sequestration and Enhanced Oil Recovery P0115 A050 Elemental and Isotope Geochemistry G0001, G1002 A060 Energy Resources G0103, G0106, G0302, G0305, G0705, G0808, G0812, G0902, G0905, G1005, G1006, P0107, P0213 A070 Environmental Characterization and Monitoring G0502, G0602, G0701, G0711, G0712, G0803, P0103, P0104, P0217 A080 Environmental Geology and Natural Hazards G0815, P0103 A090 Geodesy G0909 A100 Geodynamics, Tectonics, Structural Geology G0401, P0117 A110 Geographic and Geoscience Information Systems (GIS) G0104, G1006, P0104, P0221 A130 Geomorphology and Quaternary Geology G0104, G0303, G0714 A140 Geophysics and Petrophysics 155
G0105, G0107, G0109, G0806, G0902, G0903, G0909, G0910 A150 Geoscience Databases G0603, G0703, G0807, P0221 A160 Hydrogeology, Groundwater Modeling and Contaminant Transport G0103, G0701, G0810, G0901, P0212, P0216 A170 Mineral Prospection Mapping G0105, G0204, G0306, G0402, G0403, G0404, G0702, G0801, G0802, G0803, P0209, P0211 A190 Mineral Resources and Geometallurgy G0109, G0202, G0304, G0403, G0404, G0503, G0704, G0705, G0708, G0710, G0713, G0806, G0814, P0105, P0109, P0112, P0215, P0220 A210 Petrology, Mineralogy and Crystallography P0218 A220 Remote Sensing, Electromagnetic Data (incl. Spectral, Hyperspectral, SAR) G0810, G0811, G0815 A230 Rock Fabric Analysis and Modelling G0102, G0805, G1001 A240 Rock Mechanics and Fracture Modeling G0301, G0804, G0812, G0814, G0908 A250 Rock-Soil Mechanics and Geomechanics G0904, G0908 A260 Sedimentology, Stratigraphy and Basin Analysiss G0101, G0106, G0906, P0208 A270 Seismology G0809, G1007 A280 Soil Science G0201, G0501, P0116 A290 Volcanology and Geothermal Research G0306, G0907 A300 Other fields G0002, G0003, G0004, G0005, G0006, G0108, G0203, G0601, G0706, G0709, G0801, G0813, G1004, G1008, P0114, P0205 156
157
Author Index Aatos Soile, 52 Abderrahman Zerguine, 76 Abrykosov Petro, 65 Agou Vasiliki, 127 Agterberg Frits, 35, 113 Ahmad Mubasher, 62, 66 Ahmed Nisar, 62, 66 Allard Denis, 125 Amini Shohreh, 57 Anderson Erik, 71 Anggraini Olga Padmasari , 25 Anquez Pierre, 22 Antropov Andrey, 105 Argentin Anne-Laure, 92 Arnold Richard, 69 Aydin Orhun, 39 Bachmann Florian, 42, 69 Kai, 45 Bailleul Julien, 89 Bajat Branislav, 27 Balamurali Mehala, 54
158
Bardy Gaetan, 57 Bardzinski Piotr, 93, 95 Barth Andreas, 132, 133 Baxevani Anastassia, 126 Belozerov Boris, 88, 106, 107 Benndorf Joerg, 32 Benold Christian, 78 Bhardwaj Shivam, 37 Blunt Martin J., 106 Boamah Daniel, 133 Kwame, 133 Bock Peter, 132 Bonduà Stefano, 79 Bongaerts Jan C., 41 Bonneau François, 64 Borowka Ryszard K. , 122 Bossew Peter, 114 Braga Luis, 99 Brandmeier Melanie, 52 Brandsegg Kristian B., 105 Brosig Andreas, 132, 133 Bruno Roberto, 79
Buccianti Antonella, 99 Bukhanov Nikita, 88, 106 Nikita V., 107 Burgess Peter , 149 Burkin Joel N, 140 Butler Kevin, 39 Kevin A., 125 Bábek Ondřej, 101 Bøe Harald W., 105 Cabrera Irving , 52 Caers Jef, 114 Cao Ying, 57 Carranza Emmanuel John, 111, 115, 129, 131 Caumon Guillaume, 19, 22, 61, 151 Chandrasekhar Enamundram, 37 Chang Yuwen, 63 Chanier Frank, 89 Chauveau Benoit, 150 Chen Guanghui, 91 Guoxiong, 142 Hongjun , 122 Jianping, 144 Qi, 88 Qiyu, 21, 22, 46 Xueen, 120 Zi, 61 Cheng Qiuming, 35, 80, 90, 94, 120 Choe Jonggeun, 33, 108 Christ Alina-Berenice, 150 Chung Chang-Jo, 111 Ciski Mateusz , 146
Clausolles Nicolas, 61 Claussmann Barbara, 89 Collins Daniel, 136 Collon Pauline, 61 Corral-López Jesus, 70 Courtade Sergio, 89 Coyan Joshua, 130 Crne Alenka, 105 cui ning, 144 Cupola Fausto, 61 Dagasan Yasin , 47 Dawei Li, 83 de Fouquet Chantal, 79 de la Varga Miguel, 109 de Souza Filho Carlos Roberto, 129, 131 de Sá Vitor Ribeiro, 113 Demidov Denis, 62 Demyanov Vasily , 87, 88, 103–105 Deng Junjie, 75 Dimitrakopoulos Roussos, 18, 45 Dowd Peter, 43, 48, 91 Driesner Thomas, 64 Drinovsky Steven, 57 Duan Taizhong, 77, 148 Dubrule Olivier, 23, 106 Ducros Mathieu , 64
159
Dudzinska-Nowak Joanna , 75, 122 Duffaut Kenneth, 105 Egorov Dmitry, 106 Dmitry V., 107 Egozcue Juan José, 27, 70, 79, 98 Emery Xavier, 25 Engle Mark A. , 17, 100, 133 Ersfolk Johan, 23 Erten Oktay, 47 Ertunc Gunes, 104 Etzold Sven Heico, 132 Fabbri Andrea G., 111 Faille Isabelle, 64 Fačevicová Kamila, 101 Feldens Peter, 122 Fideles Helena MR, 81 Filzmoser Peter, 97, 99 Fioravante de Siqueira Alexandre, 58 Fišerová Eva, 39 Flores-Orozco Adrian, 78 Friedl Barbara, 92 Gadre Vikram M., 37 Gallistl Jakob, 78 Gawthorpe Rob, 149 Gerke Kirill, 81, 136 Gervais Veronique, 148 160
Gkafa Giota, 127 Godoy Vanessa, 93 Golitsyna Maria, 106 Gostick Jeff, 53 Gozzi Caterina, 99 Graffelman Jan, 70 Granjeon Didier, 148, 150 Gravey Mathieu, 47 Griffiths Cedric, 82, 147 Groh Andreas, 122 Grunsky Eric, 28 Guedes Sandro, 58 Guignard Fabian, 103 Guo Ke, 100 Qiulin, 32, 92, 143 Gupta Ashok Kumar, 34 Gutzmer Jens, 45 Gómez-Hernández J. Jaime, 61, 93 Haacke Jon E., 46 Haddad-Martim Paulo, 131 Haines Linda, 43 Halotel Julie, 104 Hampson Gary J., 23 Hamza Abdul Saboor, 41 Han Shuai, 53 Harff Jan, 75, 119, 121, 122 Hatvani István Gábor, 70
Havenith Hans-Balder, 115 He Binxian, 88 Heriawan Mohamad Nur, 25 Hielscher Peggy, 132 Hodzic Migdat, 66, 86 Holden Eun-Jung, 140 Hollis Cathy, 149 Holodnik Krzysztof, 78 Hong Runhuai, 88 Hopfner Mario, 42 Hossein Morshedy Amin, 79 Hou Jiagen, 31 Hristopulos Dionissios, 125–127 Hron Karel, 101 Hölbling Daniel, 92 Janikas Mark, 39 Jasiewicz Jaroslaw, 126 Jasper Heinrich, 42 Jensen Tue-Holm, 107 Jeong Hoonyoung, 33 John Cédric M., 23 Johnston Brian, 49 Jupp Peter E., 69 Jurdziak Leszek, 40, 93, 95 Kaczmarek Wojciech, 78 Kadyrov Rail I., 81
Kallmeier Enrico, 132 Kallunki Janne, 134 Kanevski Mikhail, 103 Kang Byeongcheol, 33, 108 Kano Nakaret , 86 Kapralova Veronika, 59 Karsanina Marina V., 81, 136 Kasmaeeyazdi Sara, 79 Katherine Silversides, 51 Kaufmann Henrik, 133 Kawalec Witold, 40, 93, 95 Kebotsamang Kago, 43, 75 Kennedy Ivan , 61, 66, 86 Kern Marius, 49 Zoltán, 70 Khalajmasoumi Masoumeh, 36 Khalid Perveiz, 62, 66 Kharyba Elena, 105 Khirevich Siarhei, 81, 136 Khramchenkov Eduard, 62 Maxim, 62 Kim Jaejun, 108 Junyi, 33 Knobloch Andreas, 132, 133 Koestel Johannes, 81 Kolehmainen Mikko, 52 Korhonen Kimmo, 134 Korost Dmitry V., 136
161
Kost Samuel, 51 Kotov Sergey, 121 Krieger Markus H., 24 Krivoruchko Konstantin, 125 Krupko Nataliia, 45, 49 Król Robert, 40, 93, 95 Kuhn Thomas, 133 Kumpan Tomáš, 101
Liu
Laib
Ma
Mohamed, 103 Laine Eevaliisa, 23, 52 Lamontagne Sébastien, 136 Lane Stuart N., 32 Lantuéjoul Christian, 19 Le Blevec Thomas, 23 Lecomte Isabelle, 149 Lee Kyungbook, 33, 108 Taehun, 108 Legler Claus, 132 Leng Shuai, 141 Li Heng, 53 Mingchao, 53 Tongfei, 91, 141 Yang, 21 Lindsay Mark D, 140
Xiaogang, 46, 55 Maciąg Łukasz, 121 Madani Nasser, 25 Maerten Frantz, 151 Mahieux Geoffroy, 89 Malencic Luka, 105 Mallants Dirk, 136 Malvesin Emmanuel, 151 Manifold Lucy , 149 Mariethoz Gregoire, 17, 32, 46, 47 Martín-Fernández J.A., 98 Josep A., 100, 133 Masiero Isabella, 149 Mazuyer Antoine, 64 McArthur Adam, 89 McKenna Sean A., 56 McKinley Jennifer, 28, 29, 49, 97 McLaughlin J. Fred, 133 McLing Travis, 133
162
BingLi, 100 Bingli, 94 Gang, 21, 22, 46 Hengguang, 87 Huan, 77 Jiannan, 144 Xiangchong, 143 Yanfeng, 148 Yue, 35 Yuming, 31 Zhuosheng, 144 Luo Lin, 88 Luppens James A., 46
Mejer Hansen Thomas, 107 Melkumyan Arman, 54, 58 Menafoglio Alessandra, 32, 39, 48 Menzel Peter, 45 Metivier Jean-Michel, 44 Mi Shiyun, 92 Middleton Maarit, 52, 97, 134 Mihalasky Mark J., 129, 135 Miksova Dominika, 97 Miluch Jakub, 122 Minniakhmetov Ilnur, 45 Mohammad Torab Farhad, 79 Mohammadzadeh Mohsen, 72 Mokryak Alexander, 87 Molayemat Hossein, 79 Molnár Ferenc, 131 Mondal Shovana, 34 Montgomery Justin B., 55 Morgan Eugene C, 56 Mosser Lukas, 106 Mostefa Belhadj Aissa, 76 Mudelsee Manfred, 119 Mueller Christina, 24 Ute, 18, 27, 28, 43 Namysłowska-Wilczyńska Barbara , 76 Nathan David, 140 Nergaard Grinde Johanne, 149
Neupane Ghanashyam, 133 Ni Yugen, 122 Niiranen Tero, 134 Nikolić Mladen, 27 Nye Charles W., 133 Nykänen Vesa, 52, 129, 131, 134, 139 O’Sullivan Francis M., 55 Occhipinti Sandra A, 140 Ogryzek Marek, 146 Olea Ricardo A. , 46, 98, 100 Ortego Maribel, 70 Osadczuk Andrzej, 122 Osmonalieva Oksana, 106 Paelike Heiko, 121 Pail Roland, 65 Pardo-Igúzquiza Eulogio, 48, 91 Park Changhyup, 108 Parnadi Wahyudi Widyatmoko , 25 Pavlidis Andreas, 127 Pawlowsky-Glahn Vera, 27, 79, 98 Pejović Milutin, 27 Pellerin Jeanne, 22 Petersen Soegun, 24 Pham Huy Giao, 86 Pigoli Davide, 48 Pingitore Nicholas E., 17 163
Pini Alessia, 39 Plavnik Andrei, 141 Pospiech Solveig, 98 Prasicek Günther, 92 Princ Tomas, 81 Prior Angel, 45 Prior Arce Angel, 32 Qin Jin, 57, 69 Quillinan Scott A., 133 Raguenel Margaux, 64 Rahimi Mana, 41, 139 Rasera Luiz Gustavo, 32 Rasilainen Kalevi, 129, 135 Rasolofosaon Patrick, 148 Raspa Giuseppe, 79 Reis Jean B.B., 99 Reitner Heinz, 78 Ren Qiubing, 53 Renard Philippe , 47 Renaudeau Julien, 151 Reshytko Alexander A., 106 Robl Jörg, 92 Rotevatn Atle, 149 Ruppert Leslie F. , 100 Římalová Veronika, 39 Römer Alexander, 78 164
Rönkkö Tero, 134 Sadeghi Behnam, 36 Sadeghnejad Saeid, 53, 90 Salomonsen Per, 89 Sandunil Kushan , 86 Schaeben Helmut, 21, 41, 42, 69, 130 Schaller Theresa, 65 Schedl Albert, 78 Scheinert Mirko, 65 Secchi Piercesare, 48 Selia Sangga Rima Roman, 130 Semenikhin Artyom, 106 Semmler Georg, 51 Seran Guntan Viliarso , 25 Shaffer Brian N., 46 Shin Hyundon, 108 Shishaev Gleb, 87, 88 Shiyun Mi, 136 Sidorov Andrei, 141 Sidorov jn Andrei, 141 Silversides Katherine L, 54, 58 Singh Manik, 65 Roshan k , 34 Sirotinskaya Susanna V., 145 Sizonenko Timofey O., 81, 136 Skou Cordua Knud, 107 Skála Jan, 101
Smilde Peter L., 24 Snehota Michal, 81 Song Suihong, 31 Srinivasan Sanjay, 56, 65 Srivastava Shalivahan, 34 Stepinski Tomasz, 126 Straubhaar Julien , 47 Strengell Juha, 134 Stulov Leonid, 105 Sun Shuang, 31 Suppala Ilkka, 23 Suzuki Norikazu, 72 Sánchez Fabrizzio, 36
Trapeznikova Olga, 116 Udegbe Egbadon, 56 Valisyev Roman V., 136 van den Boogaart K. Gerald, 27, 29, 32, 45, 49, 69, 130 Vaselli Orlando , 99 Vendeville Bruno, 89 Victorov Alexey, 116 von Storch Hans, 120
Walters Harold, 57 Wang Gongwen, 139 Jianhua, 71, 80, 143 Lu, 94 Maozhi, 94 Mingchuan, 77, 148 Wenlei, 43, 90 Tamás Xiaochen, 142 Telbisz, 91 Yongzhi, 145 Tan Yuntao, 57 Shucheng, 88 Warke Yongjie, 145 Patricia, 49 Tang Weisheng Shengquan, 120 Hou, 87 Tanos Wellmann Péter, 70 Florian, 109 Tercan Wennerström A. Erhan, 104 Marit, 23 Tetzlaff Westerholm Daniel, 89, 147, 150 Jan, 23 Thiart Wieczoreck Christien, 43 Björn, 42 Tinti Willien Francesco, 79 Françoise , 64 Tolosana-Delgado Wu Raimon, 27, 29, 31, 32, 45, 97, 98, Jiaxue, 75 130 Kang, 57, 69 Topal Lin, 77 Erkan, 47 Xuechao, 21, 22 Torppa Johanna, 131 Xi Traby Jing, 88 Renaud, 64 Xia Qinglin, 91, 141, 142 165
Xiao Fan, 60, 85, 112 Keyan, 144 Xie Shuyun, 80 Xiong Ping , 122 Xu Deyi, 80 Teng, 61 Xianguang, 71 Yan Wei, 63, 92 Haijun, 71 Yang Haiying, 88 Liang, 114 Zhaoying, 57, 69 Yao Wang, 57, 69 Yarus Jeffrey, 57 Youhua Wei, 77 Younesian-Farid Hossein, 90 Yu Jingdu, 32, 92, 143 Xianchuan, 57, 69 Xiaotong, 60, 85 Zahmatkesh Samira, 72 Zanini Andrea, 61 Zehner Bjoern, 82 Zhan Ying, 57, 69 Zhang Chaosheng, 41 Dehui, 143 Jiyin, 22 Junqiang, 95 Shengyuan, 90 Wei, 71, 80 Wenbiao, 148 Wenyan, 75, 122 Zhao Jie, 43, 90 Mengyang, 141 Zhifang, 88
166
Zheng Man, 32, 71, 80, 92, 143 Tiancheng, 87 Zhenwen He, 55 Zhijun Chen, 21 Zhong Fonan, 22 Zhou Di, 119, 122 Kefa, 35 Xiangquan, 77 Yongzhang, 60, 85 Zhu Pingping, 94 Zingerle Philipp, 65 Zuo Renguang, 115, 129 Zuquette Lázaro , 93 Źróbek Ryszard , 146
