Application of multifractal modeling for the ...

Application of multifractal modeling for the identification of alteration zones and major faults based on ETM+ multispectral data Ramin Aramesh Asl, Peyman Afzal, Ahmad Adib & Amir Bijan Yasrebi

Arabian Journal of Geosciences ISSN 1866-7511 Arab J Geosci DOI 10.1007/s12517-014-1366-2

1 23

Your article is protected by copyright and all rights are held exclusively by Saudi Society for Geosciences. This e-offprint is for personal use only and shall not be selfarchived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”.

1 23

Author's personal copy Arab J Geosci DOI 10.1007/s12517-014-1366-2

ORIGINAL PAPER

Application of multifractal modeling for the identification of alteration zones and major faults based on ETM+ multispectral data Ramin Aramesh Asl & Peyman Afzal & Ahmad Adib & Amir Bijan Yasrebi

Received: 20 December 2013 / Accepted: 5 March 2014 # Saudi Society for Geosciences 2014

Abstract The aim of this study is to investigate the reconnaissance of alteration zones and faults in Hashtjin 1:100,000 sheet (NW Iran) using concentration-area (C-A) fractal model based on remote sensing data, which has been extracted from enhanced thematic mapper (ETM)+ multispectral images. There are Oligocene volcano-plutonic rocks and TaromHashtjin metallogenic zone with Cu, Au, and Pb-Zn occurrences in the studied area. The concentration-area (C-A) fractal model proposed in this paper for the interpretation of pixel value distribution spatial patterns based on the extracted data from ETM+ multispectral images. The pixel values were calculated by the PCA (principal component analysis) method for iron oxides and argillic alteration. Furthermore, the sharpen-filtering has been applied to calculate the value pixels for the main fault zone in the Hashtjin area. The C-A model can be used to establish power-law relationships between the area and the pixel value. The log-log C-A plots show multifractal nature for iron oxides, argillic alteration zones, and faults. Results obtained by the fractal model reveal that alteration zones and major faults have a NNW-SSE trend. The alteration zones and major faults have a strong correlation with the geological map of the area.

Keywords Concentration-area (C-A) fractal model . PCA . Multispectral . Hashtjin . Iran R. A. Asl : P. Afzal : A. Adib Department of Mining Engineering, Faculty of Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran P. Afzal (*) : A. B. Yasrebi Camborne School of Mines, University of Exeter, Penryn, UK e-mail: [email protected]

Introduction Multispectral images have been increasingly utilized to characterize features on the earth’s surface for various purposes especially geosciences and mineral exploration. Remote sensing techniques are used for mineral exploration and geosciences in two applications: (1) geological mapping of faults, fractures, and lineaments; (2) delineation of hydrothermal alteration zones (Beiranvandpour and Hashim 2012; Khan et al. 2007; Sabins 1999). The purpose of displaying the multispectral image should not only be to provide a visual representation of the variance of images, although this has been the primary objective of most conventional methods. The color palette should reflect real-world features on the ground which must be the primary objective of employing remote sensing data. One of the main tasks involved in image processing is to classify image values into components and to establish the relationships between these components and features on the surface. These relationships can be visualized with a proper visual illustration or analyzed by means of various quantitative methods. Nevertheless, the first view of the image with an appropriate color palette can be important since it gives users the first perspective of the spatial distribution of multispectral image pixel values (Cheng and Li 2002). Principal component analysis (PCA) is a common method of analysis for correlated multivariable datasets, and the technique is widely used for multispectral image interpretation based on linear algebraic matrix operations. PCA can effectively concentrate the maximum information of many correlated image spectral bands into a few uncorrelated principal components and therefore can reduce the size of a dataset and enable effective image RGB display of its information. This links to the statistical methods for band selection that aims to select optimum band triplets with minimal inter-band

Author's personal copy Arab J Geosci

correlation and maximum information content (Liu and Mason 2009). Fractal geometry as one of the non-Euclidian geometry was established by Mandelbrot (1983) which is widely used in different branches of geosciences since 1980s, e.g., Agterberg et al. (1993), Cheng et al. (1994), Turcotte (1997), Cheng (2000), Li et al. (2003), Ali et al.

Major faults

Lithology

White and purple siliciclastics

Fan deposit

Fig. 1 Geological map of the Hashtjin area and its location in Iran

(2007), Zuo et al. (2009), Afzal et al. (2011), Wang et al. (2011), Afzal et al. (2012), Hashemi and Afzal (2013), and Yasrebi et al. (2013). The C-A fractal model has been proposed by Cheng et al. (1994) and applied for the identification of geochemical and geophysical anomalies from background (Afzal et al. 2010; Agterberg et al. 1996; Cheng 1999; Cheng

Author's personal copy Arab J Geosci Fig. 2 The final output of combining the results of the PCA method for iron oxide alteration in the Hashtjin sheet

2000; Goncalves et al. 1998; Hassanpour and Afzal, 2013; Sim et al. 1999; Zuo 2011; Mohammadi et al. 2013). Cheng and Li (2002) and Shahriari et al. (2013) utilized the method for the interpretation of multispectral images porphyry systems. In this paper, enhanced thematic mapper (ETM) + images were analyzed to classify alteration zones and major fractures by the concentration-area (C-A) fractal model in the Hashtjin 1:100,000 sheet, NW Iran. C-A fractal model The concentration-area (C-A) model serves to demonstrate the relationship correlated between the obtained results with the geological, geochemical, geophysical, and remote sensing information. Its most useful features are the simple implementation and the ability to compute quantitative anomalous thresholds (Cheng et al. 1994; Goncalves et al. 2001; Cheng

and Li 2002). Cheng et al. (1994) proposed the concentrationarea (C-A) method for separating geochemical and geophysical anomalies from background in order to characterize the distribution of elemental concentrations. This model has the general form of the following: Aðρ ≤ υÞ∞ ρ‐a1; Aðρ≥ υÞ ∞ ρ–a2

ð1Þ

Table 1 The result of the PCA method for enhancing iron oxide alteration Eigenvector

Band 1

Band 3

Band 4

Band 5

PC 1

0.455507

0.531267

0.478480

0.530401

PC 2 PC 3 PC 4

0.732929 0.207038 0.460938

0.205811 −0.561581 −0.600020

−0.275647 0.747063 −0.370098

−0.586921 −0.289239 0.539017

Author's personal copy Arab J Geosci Table 2 The result of PCA method for enhancing argillic alteration Eigenvector

Band 1

Band 4

Band 5

Band 6

PC 1 PC 2 PC 3 PC 4

0.447763 0.818644 −0.341532 0.112634

0.475057 0.122933 0.827476 −0.272933

0.531464 −0.393571 0.000766 0.750098

0.539792 −0.399764 −0.445689 −0.591755

where A (ρ) denotes the area with concentration values greater than the contour value ρ (pixel values in this paper), υ represents the threshold, and a1 and a2 are characteristic exponents. The area A (ρ) for a given ρ is equal to the number of cells multiplied by the cell area with pixel values greater than ρ. Area-concentration [A (ρ)] with pixel values greater than ρ. The breaks between straight-line segments on the log-log plot and the

Fig. 3 The final output of combining the results of the PCA method for argillic alteration in the Hashtjin sheet

corresponding values of ρ have been used as cut-offs to separate pixel values into different components, representing different causal factors, such as geological differences, geochemical processes, and mineralizing events (Goncalves et al. 2001; Afzal et al. 2010). Geological setting The area is situated in 120 Km south of Ardebil City, NW Iran (Fig. 1). The Hashtjin 1:100,000 sheet is located in the Alborz-Azerbaijan structural zone and TaromHashtjin metallogenic belt (Karimzadeh Somarin 2006). Most plutons of the Alborz-Azerbaijan zone are associated with porphyry and skarn occurrences (Hezarkhani and Williams-Jones 1998; Karimzadeh Somarin and Moayyed 2002). The mineralization of both porphyry and epithermal type in the zone is related to the Oligo-

Author's personal copy Arab J Geosci Fig. 4 The final output of combining the results of the sharpen 18 Filter for major faults in the Hashtjin sheet (a–d areas). Major fault population distribution maps based on the CA model

B

A

C

D

Miocene magmatism which may reveal a transition between these two types of deposits in response to changes in the tectonic setting during the evolution of a volcanic arc (Karimzadeh Somarin, 2006). There are Oligocene volcano-plutonic rocks such as andesite, trachy-andesite, granodiorite, and granite. However, there are Eocene sedimentary rocks consisting of marl, siltstone, limestone, and sandstone. Main rock types of the area are Eocene volcanic and volcano-sedimentary rocks such as ignimbrite and tuff (Karimzadeh Somarin, 2006). Studies by various researchers have shown that these masses are type-I granitoids. There are many metallic deposits and occurrences consisting of Cu, Au, Pb, and Zn. Application of the C-A model for ETM + image interpretation In this paper, Landsat ETM + images (bands 1–7) in the Hashtjin area were studied for the delineation of faults, iron oxides, and argillic alteration by ENVI 4.8 software package. Pixel value dimensions are 30×30 m2 in the multispectral images in this area. Initially, geometrical and radiometric

Fig. 5 Log-log graph of area versus pixel value in the Hashtjin sheet

Author's personal copy Arab J Geosci Table 3 The result of PC value of the C-A fractal method for iron oxide and major fault Zones

Background

Low intensity

High intensity

Iron oxide Major faults

0–100 0–18

100–177.8 18–64

177.8–255 64–255

noises were corrected, and vegetation was removed from the multispectral images. Then, iron oxides and argillic alteration zones were detected by the PCA method within the Crosta technique, and major faults were extracted by using sharpen 18 filter. A number of straight-line segments fitted to the points on the log-log plots, each representing a power-law relationship between the area and the cutoff pixel values.

A

D

B

E

G

Fig. 6 (A-I area). Iron oxide alteration population distribution maps based on the C-A model

C

F

Author's personal copy Arab J Geosci

Eight straight-line segments were fitted to the values by least squares. The cutoff values were calculated based on break points between the fitted segments. PC3 can show iron oxide area which is depicted in Fig. 2 and Table 1. PC4 can show argillic alteration zones in the Hashtjin ETM + image (Table 2 and Fig. 3). Based on the pixel values, C-A log-log plots were generated and, consequently, the different populations were separated, as depicted in Fig. 5. Based on the log-log plot, there are three populations (two thresholds) for iron oxides and major faults (Fig. 4). However, four populations were separated due to argillic alteration log-log plot. Straight lines fitted to the points on the log-log plots represent a power-law relationship between the area and cutoff pixel value (thresholds values) in a particular range. Additionally, the parameters

Fig. 7 Major faults obtained by the C-A model

(PC values of iron oxides, argillic alteration, and major faults) have a multifractal nature in this area. High-intensity iron oxide parts have pixel values higher than 177.8. Iron oxides with low values have pixel values between 100 and 177.8, and background has pixel values lower than 100 (Table 3 and Fig. 6). High-intensity iron oxide parts are situated in the central, NNW, and SE of the area, as shown in Fig. 6. Major faults and structural parts have pixel values higher than 64. Minor faults and low-intensity structures have pixel values between 18 and 64. Major faults obtained by the C-A model have a NW-SE trend in the Hashtjin area (Table 3 and Fig. 7). High-intensity argillic alteration parts have pixel values higher than 160. Argillic alteration parts with moderate intensity have pixel values between 100 and 160, and also a low value of the

Author's personal copy Arab J Geosci

A

B

D

E

C

F

Fig. 8 (A-I area). Argillic alteration population distribution maps based on the C-A method

alteration was indicated with pixel values between 51 and 100 (Table 4 and Fig. 8). Correlation between C-A results and geological particulars Based on the geological map of the Hashtjin 1:100,000 sheet and field observations, results derived via the C-A model were correlated. Iron oxides and argillic alterations clearly exist in the NW and central parts of the area (Fig. 9). Moreover, field observations have a good

correlation with results obtained by the C-A fractal model in the Hashtjin area since argillic alteration and iron oxides are obviously present in the central, NW, and SE of the area; also, kaolinite as the main index of argillic alteration occupies these parts of the Hashtjin 1:100,000 sheet. Major faults and structures studied by the C-A model have a good correlation with the geological map with the trend of NW-SE. Based on the results and observations, there are exploration targets for hydrothermal mineralization in the central, NW, and SE parts of the Hashtjin area.

Table 4 The result of PC value of the C-A fractal method for argillic alteration Zones

Background

Low intensity

Middle intensity

High intensity

Conclusion

Argillic alteration

0–51

51–100

100–160

160–255

The study on Hashtjin 1:100,000 sheet reveals that the C-A model for remote sensing data as a useful tool for mineral

Author's personal copy Arab J Geosci Fig. 9 Correlation between argillic and iron oxides in the field observation and results obtained by fractal model

exploration especially in reconnaissance and prospecting can be used for multispectral image interpretation. The advantages of this method are simplicity and easy computational implementation as well as the possibility to compute a numerical value of concentrations, which is the most useful criteria for cross-examination of information with numerical data from different sources, which can be hugely used in geology, geophysics, geochemical, and remote sensing. The C-A model is an applicable method that can be implemented to address various populations for image display. Unlike the most conventional methods, the C-A model generates different classes of pixel values on this basis and also puts into account the

spatial and geometrical properties of the real-world features on the ground such as alteration zones and faults. There is a positive correlation between the alteration zones and major faults from the C-A model, geological map, and field observations in the Hashtjin1:100,000 sheet. Therefore, the color scheme facilitated by the ENVI software package and results via the C-A model display the remotely sensed image which does not only furnish a visual representation of the pixel values but also usually reflects the distinct properties of realworld features on the ground. New exploration targets of hydrothermal mineralization and deposits can be defined in central, NW, and SE parts of the Hashjin 1:100,000 sheet.

Author's personal copy Arab J Geosci

References Afzal P, Fadakar Alghalandis Y, Khakzad A, Moarefvand P, Rashidnejad Omran N (2011) Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling. J Geochem Explor 108:220–232 Afzal P, Fadakar Alghalandis Y, Khakzad A, Moarefvand P, Rashidnejad Omran N, Asadi Haroni H (2012) Application of power-spectrum– volume fractal model for detecting hypogene, supergene enrichment, leached and barren zones in Kahang Cu porphyry deposit, Central Iran. J Geochem Explor 112:131–138 Afzal P, Khakzad A, Moarefvand P, Rashidnejad Omran N, Esfandiari B, Fadakar Alghalandis Y (2010) Geochemical anomaly separation by multifractal modeling in Kahang (Gor Gor) porphyry system, Central Iran. J Geochem Explor 104:34–46 Agterberg FP, Cheng Q, Brown A, Good D (1996) Multifractal modeling of fractures in the Lac du Bonnet Batholith, Manitoba. Comput Geosci 22:497–507 Agterberg FP, Cheng Q, Wright DF (1993) Fractal modeling of mineral deposits. In: Elbrond J, Tang X (eds) 24th APCOM symposium proceeding. Montreal, Canada, pp 43–53 Ali K, Cheng Q, Zhijun C (2007) Multifractal power spectrum and singularity analysis for modelling stream sediment geochemical distribution patterns to identify anomalies related to gold mineralization in Yunnan Province, South China. Geochemistry: Exploration, Environment, Analysis 7(4):293–301 Beiranvandpour A, Hashim M (2012) The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits. Ore Geol Rev 44:1–9 Cheng Q (1999) Spatial and scaling modelling for geochemical anomaly separation. J Geochem Explor 65:175–194 Cheng Q (2000) Multifractal theory and geochemical element distribution pattern. Earth Science–Journal of China University of Geosciences 25(3):311–318 Cheng Q, Agterberg FP, Ballantyne SB (1994) The separation of geochemical anomalies from background by fractal methods. J Geochem Explor 51:109–130 Cheng Q, Li Q (2002) A fractal concentration-area method for assigning a color palette for image representation. Computers &Geosciences 28: 567–575 Goncalves MA, Vairinho M, Oliveira V (1998) Study of geochemical anomalies in Mombeja area using a multifractal methodology and geostatistics. In: Buccianti A, Nardi G, Potenza R (eds) Proceedings of International Association for Mathematical Geology Meeting, 6– 9 October, Ischia, Italy, vol 2, Vol., pp 590–595 Goncalves MA, Mateus A, Oliveira V (2001) Geochemical anomaly separation by multifractal modeling. J Geochem Explor 72:91–114 Hashemi M, Afzal P (2013) Identification of geochemical anomalies by using of number-size (N-S) fractal model in Bardaskan area, NE Iran. Arab J Geosci 6:4785–4794

Hassanpour, S., Afzal, P., 2013. Application of concentration–number (C–N) multifractal modeling for geochemical anomaly separation in Haftcheshmeh porphyry system, NW Iran. Arabian Journal of Geosciences 6:957–970 Hezarkhani A, Williams-Jones AE (1998) Controls of alteration and mineralization in the Sungun porphyry copper deposit. Iran: evidence from fluid inclusions and stable isotopes. Economic Geology 93:651–670 Karimzadeh Somarin A (2006) Geology and geochemistry of the Mendejin plutonicrocks. Mianeh. Iran Original Research Article Journal of Asian Earth Sciences 27:819–834 Karimzadeh Somarin A, Moayyed M (2002) Granite- and gabbrodioriteassociated skarn deposits of NW Iran. Ore Geol Rev 20:127–138 Khan SD, Mahmood K, Casey JF (2007) Mapping of Mulsim Bagh ophiolite complex (Pakistan) using new remote sensing and field data. J Asian Earth Sci 30:333–343 Li C, Ma T, Shi J (2003) Application of a fractal model relating concentrations and distances for separation of geochemical anomalies from background. J Geochem Explor 77:167–175 Liu, J.G., Mason, P.J., 2009. Essential image processing and GIS for remote sensing. Wiley-blackwell, 443 p. Mandelbrot, B.B., 1983. The fractal geometry of nature: W. H. Freeman. San Fransisco, 468 pp. Mohammadi A, Khakzad A, Rashidnejad Omran N, Mahvi MR, Moarefvand P, Afzal P (2013) Application of number-size (N-S) fractal model for separation of mineralized zones in Dareh-Ashki gold deposit, Muteh Complex, Central Iran. Arab J Geosci 6:4387– 4398 Sabins FF (1999) Remote sensing for mineral exploration. Ore Geol Rev 14:157–183 Shahriari H, Ranjbar H, Honarmand M (2013) image segmentation for hydrothermal alteration mapping using PCA and concentration–area fractal model. Nat Resour Res 22:191–206 Sim BL, Agterberg FP, Beaudry C (1999) Determining the cutoff between background and relative base metal contamination levels using multifractal models. Comput Geosci 25:1023–1041 Turcotte DL (1997) Fractals and chaos in geology and geophysics. Cambridge University Press, Cambridge Wang QF, Deng J, Liu H, Wang Y, Sun X, Wan L (2011) Fractal models for estimating local reserves with different mineralization qualities and spatial variations. J Geochem Explor 108:196–208 Yasrebi AB, Afzal P, Wetherelt A, Foster P, Esfahanipour R (2013) Correlation between geological and concentration-volume fractal models for Cu and Mo mineralised zones separation in Kahang porphyry deposit. Geologica Carpathica (In press), Central Iran Zuo R, Cheng Q, Xia Q (2009) Application of fractal models to characterization of vertical distribution of geochemical element concentration. J Geochem Explor 102(1):37–43 Zuo R (2011) Decomposing of mixed pattern of arsenic using fractal model in Gangdese belt, Tibet, China. Appl Geochem 26:271–273