Mineral Exploration and Alteration Zone Mapping Using Mixture ...

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 7, NO. 1, JANUARY 2014

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Mineral Exploration and Alteration Zone Mapping Using Mixture Tuned Matched Filtering Approach on ASTER Data at the Central Part of Dehaj-Sarduiyeh Copper Belt, SE Iran Mahdieh Hosseinjani Zadeh, Majid H. Tangestani, Francisco Velasco Roldan, and Iñaki Yusta

Abstract—This paper focuses on mapping jarosite and different types of alteration minerals for mineral exploration, particularly porphyry copper deposits and discriminating alteration zones with high-potential mineralization from those showing low potentials. The study area is situated at the Central Iranian Volcano-Sedimentary Complex, where the large copper deposits like Sarcheshmeh as well as numerous occurrences of copper exist. The visible near infrared and shortwave infrared (VNIR-SWIR) bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used for mineral mapping. The spectra of diagnostic alteration mineral groups, including sericite–illite, pyrophyllite–alunite, kaolinite–dickite, chlorite–calcite–epidote, and jarosite were extracted from VNIR-SWIR bands of the ASTER imagery. These spectra were used for mineral identification through mixture tuned matched filtering (MTMF) algorithm. Results showed that alteration minerals, and the areas containing jarosite were discriminated from the surrounding districts, which illustrate the ASTER ability to provide information on the occurrence of these minerals. Identification of these areas is valuable for mineral exploration, discrimination of strong pyritization, gossans, and the mine tailings. Results also support the role of MTMF as an effective image processing technique for mineral mapping and exploration. Index Terms—Alteration minerals, ASTER, mixture tuned matched filtering (MTMF), mineral exploration, remote sensing.

I. INTRODUCTION

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EMOTE sensing is widely used for mineral exploration and permits the recognition of alteration minerals with lower cost, time , and manpower [1]–[3]. Hydrothermal alteration zones are commonly associated with certain minerals, such as propylitic assemblage (chlorite, epidote, and calcite), argillic minerals (kaolinite, dickite, montmoManuscript received January 11, 2013; revised March 24, 2013; accepted April 23, 2013. Date of publication May 29, 2013; date of current version December 18, 2013. This work was supported in part by Research and Development Center of National Iranian Copper Industries Company (NICICO). M. Hosseinjani Zadeh and M. H. Tangestani are with the Department of Earth Sciences, Faculty of Sciences, Shiraz University, 71454 Shiraz, Iran (e-mail: [email protected]; [email protected]). F. Velasco Roldan and I. Yusta are with the Departamento de Mineralogía y Petrología, Facultad de Ciencia y Tecnología, Universidad del País Vasco (UPV/ EHU), Bilbao E-48080, Spain (e-mail: [email protected]; i.yusta@ehu. es). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSTARS.2013.2261800

rillonite), phyllic alteration minerals (sericite, illite), and advanced argillic minerals (alunite, pyrophyllite). The mature gossans, which have anomalies of Au, Cu, and Mo, contain abundant hematite with variable amounts of goethite and jarosite and may have been developed over the pyritic shell of most porphyry copper deposits (PCDs) [4]. The alteration minerals mostly show diagnostic absorption features in the SWIR region of the electromagnetic spectrum, which offer potential for mineral identification (especially gold and copper) through remote sensing studies [5], [6]. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument has a great potential for mapping alteration minerals at low cost with high accuracy. The SWIR bands of ASTER allow detailed spectral characterization of surface targets, particularly alteration minerals. This sensor has considerable capability for identification of altered mineral assemblages including alunite–pyrophyllite, kaolinite–montmorillonite, illite–sericite, chlorite–epidote–calcite, and jarosite. Investigation of different spectral libraries in ENVI software and field reflectance measurements show that kaolinite, dickite, and halloysite (argillic zone) are characterized by a doublet-shaped diagnostic absorption features near 2.165 and 2.205 m (coinciding ASTER bands 5 and 6). Sericite and illite, which are indicators of phyllic alteration zone show an intense absorption feature around 2.205 m (coinciding ASTER band 6). Epidote, chlorite, and calcite (propylitic zone) exhibit diagnostic absorption feature at 2.35 m, which coincide ASTER band 8. Jarosite, which is a hydrous sulfate of potassium and iron shows absorption feature at 2.26 m (corresponding ASTER band 7). Many studies reported the importance of remote sensing for mapping alteration minerals with ASTER data through image processing techniques, such as band rationing, principal components analysis (PCA), linear spectral unmixing (LSU), matched filtering (MF), mixture tuned matched filtering (MTMF), and constrained energy minimization (CEM) [7]–[14]. Most of these studies determined hydrothermally altered minerals at regional scale through per pixel analysis with little attention to subpixel analyses. However, an image pixel is often a mixture of the energy reflected or emitted from different materials that cannot be detected by per pixel classification algorithms. Rare publications are available for mapping alteration minerals using subpixel algorithms [7], [10], [11], [14]. Although these researchers applied subpixel methods, including CEM, MTMF,

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ZADEH et al.: MINERAL EXPLORATION AND ALTERATION ZONE MAPPING USING MIXTURE TUNED MATCHED FILTERING APPROACH

LSU, and MF alteration minerals were differentiated without attention to subpixel abundances. However, mineral abundances may be used as a clue to discriminating hydrothermal alteration zones of high-potential mineralization from those with low potentials. Lowell and Guilbert [15] demonstrated that mineralization zones are conformable to the alteration zones such that the ore bodies (with a 0.5% Cu cutoff) generally overlap potassic and phyllic zones and extension and intensity of alteration can suggest the intensity of mineralization. More investigations are needed to determine whether it is possible to distinguish high-potential mineralization by remote sensing. On the other hand, no differentiation between phyllic and argillic alteration zones associated with porphyry copper mineralization was proposed in these studies. However, the unique absorption features of kaolinite (bands 5 and 6) and sericite (band 5) provides the possibility to discriminating these mineral groups. In addition, the ability of ASTER data for identification of jarosite was not considered in these studies. To the authors’ knowledge, the only study that used ASTER data for identification of jarosite through spectral techniques was Bedini [10]. This mineral can be used as indicator for identification of strong pyritization, gossans and the mine tailings. The main objectives of this research was 1) to map and discriminate different types of alteration minerals as well as areas containing jarosite (e.g., mine tailings, gossans, and pyrite zone) via ASTER data processing using mixture tuned matched filtering (MTMF) algorithm, and 2) to discriminate hydrothermal alteration zones with high-and low-potential mineralizations. Hosseinjani and Tangestani [11] compared linear spectral unmixing (LSU) and MTMF algorithms for mapping alteration minerals at Sarduiyeh area. Their study revealed the importance of mixture tuned matched filtering in identification of alteration minerals. However, differentiation between kaolinite and sericite, and discrimination of jarosite was not accomplished in their research. Discrimination of these minerals is important for differentiation of phyllic and argillic zones and identification of strong pyritization, gossans, and the mine tailings. In this study, another area at the Dehaj-Sarduiyeh Copper Belt, which contains numerous copper occurrences with considerable economic potential was investigated with the aims of mapping alteration minerals, differentiating areas containing kaolinite and sericite, discriminating jarosite and detecting hydrothermal alteration zones with high-potential mineralization. The study area is a potential zone for exploration of porphyry copper deposits in which most of the important porphyry copper deposits of Iran are situated. Results were verified by field observations, laboratory analyses, as well as a comparison of them to the available geological and alteration maps. II. GEOLOGY AND MINERALIZATION The study area is situated at the southern part of the central Iranian volcano-sedimentary complex, southeastern Kerman province, Iran [Fig. 1(A)]. It is central part of the Kerman copper district, namely Dehaj-Sarduiyeh volcano-sedimentary belt [Fig. 1(B)] and has considerable economic potential for exploration of minerals. Sarcheshmeh PCD, which is the largest copper mine of Iran, and numerous occurrences of copper, such as Darrehzard, Sereidun, and Kuhepanj, are located at

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Fig. 1. (A) Geographical location of the study area in Iran. (B) Geological subdivision map of Kerman region and the locations of porphyry copper deposits in Dehaj-Sarduiyeh belt (modified from [16]). (C) Geological map of the study area and locations of copper occurrences (modified from [16], [19], [20]).

the study area. Sericitization, propylitization, argillization, and silification are the most common types of alterations in the area. Hydrothermal alterations are developed both in the intrusive bodies and in the surrounding volcanic rocks [16], [17]. A brief introduction to the alteration zones at Sarcheshmeh, Sereidun, and Darrehzar are described below. The Sarcheshmeh porphyry copper deposit is located 160 km southwest of Kerman. Hydrothermal alteration and mineralization at Sarcheshmeh are centered on the granodiorite stock. Early hydrothermal alteration was predominantly potassic and propylitic, followed by phyllic, silicic, and argillic alterations [18]. They are arranged with respect to their spatial distribution from the center of the deposit to the periphery, including potassic, potassic affected by phyllic, strongly phyllic, and propylitic [Fig. 2(A)]. The alteration types at Sereidun prospect are manifested by the early chlorite-epidote (propylitic), the transitional quartz-sericite (phyllic), the quartz-clay (argillic), the late quartz–alunite–pyrophyllite (advanced argillic), and the quartz pyrophyllite (silicic). The phyllic alteration zone is extensively developed throughout the prospecting rocks with

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Fig. 3. Selected pixel spectra taken from ASTER VNIR and SWIR bands.

Fig. 2. (A) Alteration pattern in the Sarcheshmeh deposit (modified from [29]). (B) Hydrothermal alteration map of the Sereidun copper prospect (modified from [21]).

disseminated advanced argillic and argillic alterations bounded by propylitized rocks at east, south, and western parts of the area [Fig. 2(B)] [21]. Darrehzar deposit is located 8 km southeast of the Sarcheshmeh copper mine. Potassic, phyllic, argillic, propylitic, and silicic alterations are commonly reported such that the phyllic alteration overprints potassic in the central to western parts of the area. The hydrothermally altered rocks are highly fractured, and supergene alteration has produced a large amount of limonitic rocks, extensive oxidation, and leaching of sulfides, giving a characteristic reddish or yellowish color to the altered rocks [17]. III. ASTER DATA ANALYSIS The ASTER data used in this study was level 1B, acquired on August 9, 2004. The crosstalk correction was implemented on the SWIR bands in order to remove the effects of energy overspill from band 4 into bands 5 and 9 [22]. The VNIR and SWIR datasets were resampled and stacked into one file so that all nine bands have the same 15 15 m pixel size to preserve the spatial features provided in the VNIR bands. A subset corresponding to the study area was derived for analytical procedures. The Internal Average Relative Reflection (IARR) method was used for normalizing the integrated datasets [23]. Spectra of diagnostic alteration minerals, including sericite–illite, pyrophyllite–alunite, kaolinite–dickite, chlorite–calcite–epidote, and jarosite were extracted from imagery and were used for mineral identification through MTMF algorithm (Fig. 3). The spectrum with diagnostic absorption feature in band 6 was much the same as spectra of sericite–illite. The spectrum with a low absorption feature in ASTER bands 5 and stronger absorption in band 6 was comparable to characteristics of kaolinite–dickite. The spectrum with absorption in band 5 was indicative of alunite–pyrophyllite group, and the absorption in band 8 showed properties of chlorite–calcite–epidote group, and the spectrum that contained an absorption feature in band 7 was comparable to the spectrum of jarosite. These spectra were then used to identify the areas that contained altered minerals.

MTMF is a partial subpixel unmixing hybrid method based on the combination of well-known signal processing methodologies and linear mixture theory. This method combines the strength of the matched filter (MF) method (no requirement to know all the end-members) with physical constraints imposed by mixing theory (the signature at any given pixel is a linear combination of the individual components contained in that pixel). The results of MTMF are presented as two sets of gray images for each end member, including the MF image score and the infeasibility image. The MF image provides a means of estimating relative degree of match to the reference spectrum and the approximate subpixel abundance, with values from 0 to 1.0 [23]–[26]. IV. RESULTS AND DISCUSSION Subpixel analysis of ASTER VNIR-SWIR data yielded semi-quantification of altered minerals. In order to get satisfactory results and select pixels that matched well with the reference end members, the MF scores and infeasibility bands were used to create a 2-dimensional scatter plot. To produce a mineral classification map and the mineral fractions at each pixel, pixels of low infeasibility and MF scores higher than 0.35 were highlighted and subdivided into three groups: 0.35–0.50, 0.50–0.75, and 0.75–1. These values indicated the percentages of each mineral at pixel; for instance, the value of 0.25 showed that 25% of pixel contained the selected mineral. Regions of interest (ROI) matched to these pixels were defined, assigned a unique color, and were overlaid an ASTER gray scale imagery (Fig. 4). The discriminated areas successfully corresponded to the alteration zones around the mineral occurrences and mined porphyry copper deposits [16], [27], [14], including Sarcheshmeh and Darrezar mines, as well as Kuhepanj, Sereidun, Sarbagh, Baghkhoshk, and Dehsiahan occurrences. In addition, two altered areas at the southeast of the Darrehzar and northeastern of the area, reported by Beiranvand Pour and Hashim [14], were discriminated [Fig. 4(A), prospect 1 and 2]. Two new altered areas at northwest and southwest of the Sarcheshmeh were also identified [Fig. 4(A), prospects 3 and 4]. Investigation of discriminated minerals revealed that sericite–illite is the dominant alteration mineral group exposed at the area. The discriminated minerals at most occurrences, including Sarcheshmeh, Sereidun, Darrehzar, and Kuhepanj showed circular to elliptical pattern with illite–sericite as

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TABLE I LOCATION, GRADE, AND SIZE OF SOME OF PORPHYRY COPPER DEPOSITS IN STUDY AREA (EXTRACTED FROM [16] AND [28])

Fig. 4. (A) Final classification image map of alteration minerals for MTMF algorithm at the study area, (B) Sarcheshmeh mine, (C) Sereidun, and (D) Darrehzar. Ja–Ser, Ser–Il, Pyr–Alu, Kao, and Ch–Ep–Ca indicate jarosite–sericite, sericite–illite, pyrophyllite–alunite, kaolinite, and chlorite–epidote–calcite, respectively.

dominant zone, scattered kaolinite–dickite and sparse alunite–pyrophyllite, surrounded by a combination of epidote, chlorite, and calcite (Fig. 4). Areas discriminated by the use of jarosite spectrum corresponded well with the distribution of gossans in altered systems. In most cases, jarosite was distributed over pixels, which were also discriminated as sericite–illite, and rarely over kaolinite pixels. This confirms the occurrence of jarosite and goethite bearing gossans over the pyritic shell of most Iranian porphyry copper deposits [4]. In addition, pixels that were discriminated as jarosite corresponded partly to the location of mine tailings such as east and northeast Sarcheshmeh and south of Darrehzar [Figs. 4(B), and (D)]. The fraction of this mineral in each pixel was mostly less than 0.50 except in a low number of pixels at central and south parts of the Darrehzar, which was 0.5–0.75 and 0.75–1, respectively. Low existence of jarosite can be attributed to the occurrence of small amounts of this mineral at the surface (lower than spatial resolution of the ASTER data) or its mixture with other oxide minerals, such as hematite and goethite. Atapour and Aftabi [4] stated that gossans overlying Sarcheshmeh porphyry copper deposit contain abundant hematite with variable amounts of jarosite and goethite. Furthermore, discriminated pixels of jarosite at Darrehzar are much more than the Sarcheshmeh [Figs. 4(B) and (D)]. Field works also confirmed the existence of the extensive oxidation zone at Darrehzar. Low amount of jarosite at Sarcheshmeh could be attributed to the exploration activity at this mine, which has caused transferring of the oxide zone. The MTMF results provide an estimate to mineral subpixel fractions leading to the abundances of alteration minerals at each pixel. In order to determine whether the fractions correspond to the grade, size, and intensity of alteration minerals, eight copper deposits were investigated (Table I). High fractions (0.75–1) of sericite, kaolinite, jarosite, and chlorite were found at Sarcheshmeh, Darrehzar, and Nowchon ore deposits, which are giant or medium with high grades. In addition, prospects 3

and 4 show high fractions of chlorite (0.75–1), which verifies the propylitic alteration. In general, the areas with high economic potential are associated with mineral fractions from high to low (1–0.35). Small and low-grade deposits such as Sereidun, Kuhpanj, Baghkhoshk, and Sarbagh are associated with moderate to low fractions (0.75–0.35) pixels. Prospect 1 is associated with moderate to low fractions, which may suggest a small low-grade deposit. Field investigations at this area revealed weak to moderate alterations; one sample showed occurrence of pyrite and low amounts of chalcopyrite. Dehsiahan, which is identified as a deposit with no economic importance for porphyry copper (Geological Survey of Iran, 1973a), is associated with low-fraction (0.35–0.5) pixels. Chlorite was discriminated at the south of the area with low fractions (0.35–0.5) [Fig. 4(A)]. These locations mostly corresponded to the poorly consolidated sandstone [Fig. 1(C)]. V. ACCURACY ASSESSMENT The accuracy of a classified image refers to the extent which it agrees with a set of reference data. A field reconnaissance was carried out at the seven altered areas, and 120 samples were collected through a stratified random sampling of fresh and surface-weathered of representative hydrothermally altered rocks of Sarcheshmeh and Darrehzar mines, Sereidun, Nowchon, Kuhepanj, Baghkhoshk occurrences, and prospect 1 [Fig. 1(C)]. The samples were used for spectral analyses, X-ray diffraction, and microscopic studies. The accuracy of discriminated minerals was assured by a) visual inspection using large-scale alteration maps of Sarcheshmeh and Sereidun and mineralogical analysis of the collected samples and b) checking the veracity of identified minerals with corresponding field samples analyzed by XRD and ASD. The visual inspection of discriminated alteration minerals show good correlation with the alteration maps [Figs. 1(C) and 4(A)].

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Alteration maps of the Sarcheshmeh and Sereidun agree well with ASTER alteration maps (Figs. 2 and 4). The ASTER alteration map of Sereidun shows extensive phyllic alteration with disseminated advanced argillic and argillic alterations bounded by propylitized rocks at eastern and southeastern areas [Fig. 4(C)]. Alterations at Sarcheshmeh are in ellipsoidal shape with dominant phyllic, and sparse argillic zone surrounded by propylitic [Fig. 4(B)]). However, mining activities have caused disorder at Sarcheshmeh outcrops. Some of the discriminated areas at east and west of Sarcheshmeh correspond to the location of mine tailings. In addition, the center of elliptical discriminated area at Sarcheshmeh was not identified, which may be pinpointed to the location of potassic and biotite zones. The ASTER does not have the ability to identify these zones. Discriminated altered areas at Darrehzar also show an acceptable agreement with field criteria. Phyllic and argillic alterations are developed over most of the area, surrounded by propylitic alteration. In addition, a large number of pixels were identified as jarosite, which correspond the extensive oxide zone at Darrehzar [Fig. 4(C)]. In order to check the veracity of identified minerals, the predicted and actual class labels for a set of specific sites were compared statistically so that the identified minerals by ASTER were compared with the analyzed ground truth samples. If the result of ASTER data was similar to field data, the value 1 was adopted, and if the results were different, the value 0 was assigned to the site. Frequency is then applied to generalize the results from samples and to determine the numbers in which the results obtained from ASTER data were similar to field data. Percentages of correctly classified areas for sericite–illite, kaolinite–dickite, jarosite, alunite–pyrophyllite, and chlorite–epidote–calcite groups were 60%, 70%, 88%, 88%, and 70%, respectively. It showed that minerals characterizing the alteration zones were identified with an accepted level of accuracy. VI. SUMMARY AND CONCLUSION The alteration minerals were mapped by applying MTMF algorithm to the ASTER data using the image-extracted spectra as end members. Spatial distribution of alteration minerals revealed an excellent correlation to the altered zones and the types of alteration zones in the generalized Lowell and Guilbert porphyry copper model [15]. Discriminated minerals illustrated circular to elliptical shapes with sericite and argillic zones surrounded by propylitized rocks in known porphyry copper deposits, such as Sarcheshmeh, Darrehzar, Sereidun, and Kuhepanj. The enhanced areas for jarosite correspond the distribution pattern of iron oxides and some tailing dumps produced as a result of mining activities. Results revealed that spectral unmixing is valuable for mineral exploration. Comparisons of subpixel abundances with known mineral occurrences showed a reasonable correspondence. Such that areas with high fractions of alterations corresponded well with important mineralized districts. It is suggested that investigating abundances of alteration minerals leads to identifying alteration zones with high-potential mineralization. It is concluded that ASTER data processing with MTMF spectral unmixing algorithm proves to

be a powerful tool in quantification of alteration minerals as the initial step of ore deposit exploration. Although MTMF can be used to discriminating alteration minerals and unmixing their apparent abundances on a subpixel basis, much efforts remain to be done in other mineralized areas for verifying accurate determination of mineral fractions and to prove its ability for determination of high-potential mineralization. ACKNOWLEDGMENT The authors are sincerely grateful to the geologists and staff of the Sarcheshmeh copper mine for providing the facilities and kindly helping us during our field work. This work was funded by Research and Development Center of National Iranian Copper Industries Company (NICICO). The authors thankfully acknowledge the constructive suggestions received from the IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING Editor Prof. J. Chanussot, and four anonymous reviewers. REFERENCES [1] M. J. Abrams, D. Brown, L. Lepley, and R. Sadowski, “Remote sensing for porphyry copper deposits in southern Arizon,” Econ. Geol., vol. 78, no. 4, pp. 591–604, 1983. [2] W. Loughlin, “Principal component analysis for alteration mapping,” Photogrammetric Eng. Remote Sens., vol. 57, pp. 1163–1169, 1991. [3] A. P. Crosta and C. R. Filho, “Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, using ASTER imagery and principal component analysis,” Int. J. Remote Sens., vol. 24, no. 21, pp. 4233–4240, 2003. [4] H. Atapour and A. Aftabi, “The geochemistry of Gossans associated with Sarcheshmeh porphyry copper deposit, Rafsanjan, Kerman, Iran: Implications for exploration and the environment,” J. Geochem. Explorat., vol. 93, pp. 47–65, 2007. [5] G. R. Hunt and R. P. Ashley, “Spectra of altered rocks in the visible and near infrared,” Econom. Geol., vol. 74, pp. 1613–1629, 1979. [6] R. N. Clark, “Spectroscopy of rocks and minerals and principles of spectroscopy,” in Manual of Remote Sensing, A. N. Rencz, Ed. New York, NY, USA: Wiley, 1999, ch. 1, pp. 3–58. [7] X. Zhang, M. Pazner, and N. Duke, “Lithologic and mineral information extraction for gold exploration using ASTER data in the south Chocolate Mountains (California),” ISPRS J. Photogrammetry Remote Sens., vol. 62, pp. 271–282, 2007. [8] S. Gabr, A. Ghulam, and T. Kusky, “Detecting areas of high-potential gold mineralization using ASTER data,” Ore Geol. Rev., vol. 38, pp. 59–69, 2010. [9] J. C. Mars and L. C. Rowan, “Spectral assessment of new ASTER SWIR surface reflectance data products for spectroscopic mapping of rocks and minerals,” Remote Sens. Environ., vol. 114, no. 9, pp. 2011–2025, 2010. [10] E. Bedini, “Mineral mapping in the Kap Simpson complex, central East Greenland, using HyMap and ASTER remote sensing data,” Adv. Space Res., vol. 47, pp. 60–73, 2011. [11] M. Hosseinjani and M. H. Tangestani, “Mapping alteration minerals using sub-pixel unmixing of ASTER data in the Sarduiyeh area, southeastern Kerman, Iran,” Int. J. Digit. Earth, vol. 4, no. 6, pp. 487–504, 2011. [12] H. Ranjbar, F. Masoumi, and E. J. M. Carranza, “Evaluation of geophysics and spaceborne multispectral data for alteration mapping in the Sarcheshmeh mining area, Iran,” Int. J. Remote Sens., vol. 32, no. 12, pp. 3309–3327, 2011. [13] R. Amer, T. Kusky, and A. Mezayen, “Remote sensing detection of gold related alteration zones in Um Rus area, central eastern desert of Egypt,” Adv. Space Res., vol. 49, no. 1, pp. 121–134, 2012. [14] A. B. Pour and M. Hashem, “Identifying areas of high economic-potential copper mineralization using ASTER data in the Urumieh-Dokhtar Volcanic Belt, Iran,” Adv. Space Res., vol. 49, pp. 753–769, 2012. [15] J. D. Lowell and J. M. Guilbert, “Lateral and vertical alteration-mineralization zoning in porphyry ore deposits,” Econom. Geol., vol. 65, pp. 373–408, 1970.

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[16] R. Nedimovic, “Exploration for ore deposits in Kerman region,” Geological Survey of Iran, Tehran, Iran, Rep. No. Yu/53, 1973. [17] M. Dimitrijevic, “Geology of Kerman Region, Inst. for Geological and Mining Exploration and Investigation, Beograd, Yugoslavia,” Ministry of Econ. Geological Survey of Iran, Tehran, Iran, Rep. No. 52, 1973. [18] A. Hezarkhani, “Hydrothermal evolution of the Sarcheshmeh Porphyry Cu-Mo deposit, Iran: Evidence from fluid inclusions,” J. Asian Earth Sci., pp. 409–422, 2006. [19] Geological Survey of Iran, “Geological map of Pariz, 1:100000 SHEET 7149,” Ministry of Econ. Geological Survey of Iran, Tehran, Iran, 1973. [20] Geological Survey of Iran, “Geological map of Chahargonbad, 1:100000 SHEET 7249,” Ministry of Econ. Geological Survey of Iran, Tehran, Iran, 1995. [21] H. Barzegar, “Geology, petrology and geochemical characteristics of alteration zones within the Seridune prospect, Kerman, Iran,” Ph.D. dissertation, RWTH Aachen Univ., Fakultät für Georessourcen und Materialtechnik, Aachen, Germany, 2007, pp. 180. [22] A. Iwasaki and H. Tonooka, “Validation of a crosstalk correction. Algorithm for ASTER/SWIR,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 12, pp. 2747–2751, 2005. [23] Research Systems Inc., ENVI Tutorial, ENVI Software Package. ver. 4.0, 2003. [24] J. W. Boardman, F. A. Kruse, and R. O. Green, “Mapping target signatures via partial unmixing of AVIRIS data,” in Summaries of 5th Annu. JPL Airborne Geosciences Workshop, Pasadena, CA, USA, 1995, pp. 23–26. [25] J. W. Boardman, NASA Jet Propulsion Laboratory, Pasadena, California, “Leveraging the high dimensionality of AVIRIS data for improved subpixel target unmixing and rejection of false positives: mixture tuned matched filtering,” in Proc. 5th JPL Geoscience Workshop, R. O. Green, Ed., 1998, pp. 55–56. [26] J. W. Boardman and F. A. Kruse, “Analysis of imaging spectrometer data using N-dimensional geometry and a mixture-tuned matched filtering approach,” IEEE Trans. Geosci. Semote Sens., vol. 49, no. 11, pp. 4138–4152, 2011. [27] H. Ranjbar, M. Honarmandb, and Z. Moezifar, “Application of the Crosta technique for porphyry copper alteration mapping, using ETM data in the southern part of the Iranian volcanic sedimentary belt,” J. Asian Earth Sci., vol. 24, pp. 237–243, 2004. [28] B. Shafiei and J. Shahabpour, “Gold distribution in porphyry copper deposits of Kerman region, Southeastern Iran,” J. Sci., Islamic Republic of Iran, vol. 19, no. 3, pp. 247–260, 2008.

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Mahdieh Hosseinjani Zadeh received the M.S. degrees in economic geology from the Shiraz University, Shiraz, Iran, in 2007. Since 2008, she has been working towards the Ph.D. degree at the Department of Earth Sciences, Shiraz University. She is interested in research work on the applications of remote sensing in geological mapping and mineral exploration.

Majid H. Tangestani received the M.Sc. degree in geology from Kerman University, Iran, and the Ph.D. degree in the application of remote sensing and GIS in porphyry copper exploration from Shiraz University, Iran. He is currently an Associate Professor with the Department of Earth Sciences, Shiraz University. His research area is applications of remote sensing and GIS in geosciences.

Francisco Velasco Roldan received the Ph.D. degree in geology from the University of Bilbao, Bilbao, Spain, in 1976. He is a Full Professor in mineralogy and ore geology at the University of the Basque Country, UPV/EHU, Spain. His research interests focus on the geology, mineralogy, geochemistry, the origin of the sediment-hosted base metal deposits, and volcanic massive sulphide deposits. Dr. Velasco Roldan is Associate Editor of the European Journal of Mineralogy.

Iñaki Yusta received the Ph.D. degree in geology at the University of the Basque Country, UPV/EHU, Spain in 1993. He is a Full University Teacher at the University of the Basque Country, UPV/EHU, Spain. His research interests are mineralogy and geochemistry of rocks, sediments and minerals, anthropogenic contamination and post-ining environmental impact, hydrothermal alteration in ore deposits, and mineralogical and chemical characterization of archaeological deposits.