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Seismic fault detection based on multi-attribute support vector machine analysis Haibin Di*, Muhammad A. Shafiq, and Ghassan AlRegib Center for Energy and Geo Processing (CeGP), Georgia Institute of Technology Summary Reliable fault detection is one of the major tasks of subsurface interpretation and reservoir characterization from three-dimensional (3D) seismic surveying. This study presents an innovative workflow based on multi-attribute support vector machine (SVM) analysis of a seismic volume, which consists of four steps. First, three groups of seismic attributes are selected and computed from the volume of seismic amplitude, including edge-detection, geometric, and texture, all of which clearly highlight the seismic faults in the attribute images. Second, two sets of training samples are prepared by manually picking on the faults and the nonfaulting zones, respectively. Third, the SVM analysis is performed on the training datasets that builds an optimal classification model for volumetric processing. Finally, applying the SVM model to the whole seismic survey leads to a binary volume, in which the presence of a fault is labelled as ones. The added values of the proposed method are verified through applications to the seismic dataset over the Great South Basin in New Zealand, where the dominant features are polygonal faults of varying sizes and orientations. The results demonstrate not only good match between the detected faults and the original seismic images, but also great potential for quantitative fault interpretation, such as semi-automatic/automatic fault extraction, to aid structural framework modeling and reservoir simulation in the exploration areas of numerous faults and fractures Introduction Faults and fractures are often of important geologic implications for investigating hydrocarbon accumulation and migration in a petroleum reservoir in the subsurface, and the presence of a fault can be visually recognized as a lineament of abrupt changes in reflection patterns from three-dimensional (3D) reflection seismic data. However, quantitative fault interpretation, such as patch extraction, is a time-consuming and lab-intensive process and remains as a challenging topic, especially for an exploration area featured with numerous faults. In the past decades, great efforts have been devoted into developing new attributes and methods/algorithms to help improve the accuracy of seismic fault detection. From the perspective of seismic attribute analysis, both the edge-detection and geometric attributes are applicable for fault detection, due to the apparent lateral variation of seismic waveform and/or amplitude across a fault. The seismic edge-detection analysis was first presented as the coherence attribute for highlighting the faults and

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stratigraphic features from a seismic cube (Bahorich and Farmer, 1995), and since then, such attribute and its derivatives has been improved for better detection resolution and noise robustness (e.g., Luo et al., 1996; Marfurt et al., 1998; Tingdahl and de Rooij, 2005; Al-Dossary et al, 2014; Di and Gao, 2014; Wang et al., 2016). Then for more robust fault detection and fracture characterization from 3D seismic data, the seismic geometric attributes are developed by quantifying the lateral variations of the geometry of seismic reflectors, including the second-order curvature (e.g., Lisle, 1994; Roberts, 2001; Al-Dossary and Marfurt, 2006), and the third-order flexure attributes (e.g., Gao, 2013; Yu, 2014; Di and Gao, 2017a). Meanwhile, computer-aided extraction of fault patches has been the research focus since 1990s. For example, Meldahl et al. (1999) presented a semi-automatic approach for detecting seismic chimneys by combining directive attributes and neural network, and such approach was later adapted to fault extraction from 3D seismic data by Tingdahl and de Rooij (2005). Gibson et al. (2005) and Zhang et al. (2014) proposed grouping fault points into the local planar patches and then merging these small patches into larger fault surfaces under certain geometric constraints. Hale (2013) proposed a dynamic time warping algorithm to generate fault surfaces based on the boundary constraints derived from the thinned discontinuity images. Wang and AlRegib (2014) introduced the ideas of motion vectors in video coding and processing to extract fault surfaces. Unfortunately, all these algorithms are unable to simultaneously achieve both high resolution in extracting true faults and high accuracy in avoiding non-fault artifacts. The common result is either an aggressive case with high resolution (most/all true faults extracted) but low accuracy (many artifacts introduced), or a conservative one with high accuracy (few artifacts introduced) but low resolution (few true faults extracted) (Di and Gao, 2017b). Therefore, semiautomatic/automatic fault extraction is still in the experimental phase for testing and not ready for practical implementation and application to industrial projects. For resolving such limitation, this paper first proposes a new fault-detection workflow based on semi-supervised multiattribute support vector machines (SVM) analysis, and then applies it to the 3D seismic dataset over the Great South Basin (GSB) in New Zealand, where polygonal faults of varying sizes and orientations are observed in the subsurface. Algorithm description This study adapts the binary SVM classification to work for multi-attribute seismic fault detection, and the proposed

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SVM-based fault detection

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Figure 1: The time slice of original seismic amplitude at 1152 ms over the Great South Basin (GSB) in New Zealand, where the dominant structural features are polygonal faulting in varying sizes and orientations. method consists of four steps. First, three groups of seismic attributes are selected and calculated from a seismic dataset, which expands the seismic domain into a multi-dimensional attribute domain and represents every sample with a vector. Second, two sets of training data are manually picked at both the faults and the non-faulting areas. Third, a SVM classifier is trained by the picked training datasets. Finally, the created classifier is applied to the whole seismic volume, and a binary volume is generated with the potentials faults labelled as ones.

reflector dip, the curvature (Roberts, 2001), the flexure (Di and Gao, 2017a), and the geometric fault (Di and AlRegib, 2017). Edge-detection attribute. It evaluates the lateral changes in seismic waveform and/or amplitude using various edge-detection operators, including the coherence (Bahorich and Farmer, 1995), the Sobel filter (Luo et al., 1996), the semblance (Marfurt et al., 1998), the Canny edge (Di and Gao, 2014), the similarity, and the variance (Pedersen et al., 2002). Texture attribute. It describes the local distribution of seismic texture using various statistical operators, including the GLCM contrast and homogeneity (Gao, 2003; Eichkitz et al., 2013), gradient of texture (GoT) (Shafiq et al., 2017a), and seismic saliency (Shafiq et al., 2017b).

After extracting all fourteen attributes, every sample in the seismic volume is represented by a vector of 14 elements, which allows classifying the samples in the 14-dimensional attribute domain and estimating a 13-dimensional hyperplane that well separates these samples into two classes. To be clear, all the selected attributes are normalized, due to their different units of measurement and moreover distinct histograms. B.

Training sample labelling

To ensure accurate SVM classification, two sets of training samples are manually picked on the faults (171 picks) and the non-faulting zones (722 picks) (Figure 2), which are further used in two ways. First, cross plotting of the selected attributes over the manual pickings could be used for verifying the step of attribute selection, in which the fault pickings and the non-fault pickings are partitioned into two groups with a border between them. Second, the manual pickings can help check the accuracy of the training SVM

For the convenience of illustrating the proposed workflow, we expand each step in details, using the 3D seismic dataset over the Great South Basin (GSB) in New Zealand, where the subsurface geology is dominated by polygonal faulting in varying sizes and orientations. Figure 1 displays the time section of original seismic amplitude at 1152 ms, in which a set of faults are clearly imaged. A.

Attribute selection

For efficient fault detection, the selected attributes are expected capable of enhancing the faults to be more distinguishable from the non-fault features (such as horizons) in the attribute images. Among all possible seismic attributes, we select and generate fourteen attributes from the amplitude volume, which are categorized into three groups,  Geometric attribute. It measures the lateral variation of the geometry of seismic reflectors, including the


Figure 2: The manual pickings on the vertical section of crossline 2800, including 171 pickings on the faults (denoted as cyan dots) and 722 pickings on the surrounding nonfaulting features (denoted as magenta dots).

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D.

Volumetric processing

After verifying the accuracy of the built SVM classification model, it is applied to the whole seismic survey for volumetric processing, generating a binary volume with fault labelled as ones. Results and applications

Figure 3: The re-classification of the 893 pickings by the built SVM model, overlaying the vertical section of crossline 2800, with the cyan dots representing the faults and the magenta dots representing the non-faulting features. model. Ideally, the built model should be capable of labelling these pickings accurately; however, due to the mispickings of the seeds and/or the noises present in the attributes, errors are often observed. C.

We apply the proposed multi-attribute SVM classification to the GSB seismic dataset, and for the convenience of result interpretation, we then adjust the opacity of the binary fault volume and overlay them on the original seismic images. The results are displayed in different ways, including the 3D view (Figure 4), the corresponding time slice at 1152 ms (Figure 5), and four randomly-selected vertical sections (Figure 6), all of which demonstrate good match between the detected faults and the original seismic images, indicating the accuracy of the proposed SVM fault classification.

SVM model training and testing

Based on the labelled training datasets, the SVM model is built for connecting the selected fourteen attributes and the fault labelling. Before applying for volumetric processing, the model is first verified through the training datasets. Figure 3 displays the classification of the 893 pickings. Compared to the training samples (Figure 2), we notice that the majority of the pickings are labelled corrected, except those on the zones where the seismic expressions are similar to those on the faults (denoted by circles).

Figure 5: The time slice of the detected faults at 1152 ms by the proposed multi-attribute SVM classification. Note the good match between the polygonal faults and the original seismic image.

Figure 4: 3D view of the fault detection from the Great South Basin (GSB) in New Zealand, overlaying the original seismic amplitude. Note the good match between the detected faults and the seismic images.


Besides the qualitative mapping of the faults from a seismic dataset, the binary volume could also serve as input to more advanced quantitative interpretation. For example, seimiautomatic/automatic fault extraction can be performed on the fault volume, and Figure 7 displays the extracted fault patches from the binary volume, clipped to the time slice at 1152 ms.

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Conclusions Reliable detection of subsurface faults and fractures from 3D seismic data is essential for reservoir characterization and modeling. This study has presented a new workflow for seismic fault detection based on multi-attribute support vector machine (SVM) classification, which consists of three major components. First, three attribute groups, including the edge-detection attributes, geometric attributes, and the texture attributes, are generated from the amplitude volume that help differentiate the faults of interpretational interest from the surrounding non-faulting features. Second, two sets of manual pickings are used for training a SVM model in the attribute domain and building an optimal model for volumetric processing. Finally, applying the built classification model to the whole seismic survey provides a binary volume, which not only clearly delineates the faults, but also holds the potential for more advanced fault interpretation, such as semi-automatic/automatic fault extraction. Such multi-attribute seismic feature classification could be further improved by incorporating more fault attributes, increasing the amount of training samples, and applying more advanced machine learning algorithms (e.g., CNN). However, it might require more time and efforts for data preparation and computation. Figure 6: Four randomly-selected vertical sections of the fault detection by the proposed multi-attribute SVM classification. Note the good match between the detected faults and the original seismic image.

Acknowledgments This work is supported by the Center for Energy and Geo Processing at Georgia Tech and King Fahd University of Petroleum and Minerals. We thank the New Zealand Petroleum and Minerals (NZP&M) for providing the 3D seismic dataset over the Great South Basin (GSB). The SVM classification algorithm is provided by the Turi GraphLab CreateTM under an academic license.

Figure 7: The fault patches extracted from the binary fault volume by the proposed multi-attribute SVM classification, clipped to the time slice at 1152 ms.


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