Robust Principal Component Analysis and Fractal Methods to Delineate Mineralization-Related Hydrothermally-Altered Zones from ASTER Data: A Case Study of Dehaj Terrain, Central Iran | ||
| Journal of Mining and Environment | ||
| مقاله 7، دوره 11، شماره 4، دی 2020، صفحه 1023-1037 اصل مقاله (4.01 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2020.9619.1876 | ||
| نویسندگان | ||
| N. Habibkhah1؛ H. Hassani* 1؛ A. Maghsoudi2؛ M. Honarmand2 | ||
| 1Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| 2Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran | ||
| چکیده | ||
| The Dehaj area, located in the southern part of the Urumieh-Dokhtar magmatic belt, is a well-endowed terrain hosting a number of world-class porphyry copper deposits. These deposits are all hosted in an acidic to intermediate volcano-plutonic sequence greatly affected by various types of the hydrothermal alterations, whether argillic, phyllic or propylitic. Although there are a handful of hitherto-discovered porphyry copper deposits in the area, the geological setting of the area suggests the possibility of finding further deposits. The recognition and delineation of the hydrothermal alterations can pave the way for the discovery of further potential zones that possibly host the porphyry copper deposits. The current work proposes a hybrid methodology applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery by combining the application of dimension reduction and fractal techniques to delineate the hydrothermally-altered zones In order to reduce the dimensionality of multi-band ASTER data, Robust Principal Component Analysis (RPCA) was employed to elicit the traces of hydrothermally-related mineral assemblages including illite, sericite, quartz, kaolinite, epidote, and chlorite. Highlighting the existence of the aforementioned minerals, the extracted components require interpretation, i.e. a boundary is required to constraint the hydrothermally affected zones from the rest of the geological units. In order to tackle such a challenge, the authors introduce the concept of value-pixel fractal technique for the extracted principal components. The Prediction-Area (P-A) plot is used for the validation, which shows that the identified alterations correlate with the mineralization. The results obtained are verified by a geological survey, where a number of samples are collected from the delineated zones. The samples are analyzed by the XRD techniques, finding that this work is successful in classifying the hydrothermally-altered zones. | ||
| کلیدواژهها | ||
| Robust Principal Component Analysis (RPCA)؛ Value–Pixel fractal model؛ Hydrothermal alteration؛ Porphyry copper | ||
| مراجع | ||
|
[1]. Honarmand, M., Ranjbar, H. and Moezifar, Z. (2002). Integration and analysis of airborne geophysics, remote sensing and geochemical data of Sar Cheshmeh area using directed principal component analysis. Exploration Mining Geology. 11: 43–48. [2]. Ranjbar, H. and Honarmand, M. (2004). Integration and analysis of airborne geophysical and ETM+ data for exploration of porphyry type deposits in the Central Iranian Volcanic Belt using fuzzy classification. International Journal of Remote Sensing. 25: 4729–4741. [3]. Mars, J.C. and Rowan, L.C. (2006). Regional mapping of phyllic and argillic altered rocks in the Zagros magmatic arc, Iran, using Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data and logical operator algorithms. Geosphere. 2 (3): 161-186. [4]. Tangestani, M. H., Mazhari, N., Agar, B. and Moore, F. (2008). Evaluating Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for alteration zone enhancement in a semi-arid area, northern Shahr–e–Babak, SE Iran. International Journal of Remote Sensing. 29: 2833–2850. [5]. Hosseinjani Zadeh, M. and Honarmand, M. (2017). A remote sensing-based discrimination of high-and low-potential mineralization for porphyry copper deposits; a case study from Dehaj-Sarduiyeh copper belt, SE Iran. European Journal Remote Sensing. 50 (1): 332-342. [6]. Saric, V. and Mijalkovic, N. (1973). Metallogenic map of Kerman region, 1:500000 scale. In: Exploration for ore deposits in Kerman region. Report No. 53, Tehran, Iran: Ministry of Economy Geological Survey of Iran. [7]. Waterman, G.C. and Hamilton, R.L. (1975). The Sar Cheshmeh porphyry copper deposit. Economic Geology. 70: 568–576. [8]. Shahabpour, J. and Kramers, J.D. (1987). Lead isotope data from the Sar Cheshmeh porphyry copper deposit. Mineralium Deposita. 22: 278–281. [9]. Hassanzadeh, J. (1993). Metallogenic and tectono–magmatic events in the SE sector of the Cenozoic active continental margin of Iran (Shahr–e–Babak area, Kerman province). Unpublished PhD thesis, University of California, USA. [10]. Ranjbar, H. (1996). An integrated study of remote sensing, geophysical and geochemical data in exploration for copper mineralization in the Pariz area, Kerman Province, Iran, with reference to GIS application. PhD thesis (unpublished), University of Delhi, India. [11]. Tangestani, M. and Moore, F. (2001). Comparison of three principal component analysis techniques to porphyry copper alteration mapping a case study in Meiduk area, Kerman, Iran. Canadian Journal of Remote Sensing. 27(2): 176–182. [12]. Tangestani, M.H. and Moore, F. (2002). Porphyry copper alteration mapping at the Meiduk area, Iran. Journal of Remote Sensing. 23: 4815– 4825. [13]. McInnes, B.I.A., Evans, N.J., Fu, F.Q., Garwin, S., Belousova, E., Griffin, W.L., Bertens, A., Sukama, D., Permanadewi, S., Andrew, R.L. and Deckart, K. (2005). Thermal history analysis of selected Chilean, Indonesian, and Iranian porphyry Cu–Mo–Au deposits. In: Porter, T.M. (Ed.), Super Porphyry Copper and Gold Deposits: a Global Perspective. Adelaide: PGC publishing. [14]. Shafiei, B. (2008). Metallogenic model for Kerman porphyry copper belt and its implications for exploration. PhD thesis (unpublished), Shaheed Bahonar University of Kerman, Iran. [15]. Safari, H., Bagas, L. and Shafiei Bafti, B. (2015). Structural controls on the localization of Cu deposits in the Kerman Cu metallogenic province of Iran using geoinformatic techniques. Ore Geol. Rev. 67: 43–56.
[16]. Ayoobi, I. and Tangestani, M.H. (2017). Evaluating the effect of spatial subsetting on subpixel unmixing methodology applied to ASTER over a hydrothermally altered terrain. International Journal of Applied Earth Observation and Geoinformation. 62: 1-7. [17]. Safari, M., Maghsodi, A. and Pour, A.B. (2017). Application of Landsat-8 and ASTER satellite remote sensing data for porphyry copper exploration: a case study from Shahr-e-Babak, Kerman, south of Iran. Geocarto International. 32: 1-16. [18]. Lowell, J.D. and Guilbert, J.M. (1970). Lateral and vertical alteration-mineralization zoning in porphyry ore deposits. Economic Geology. 65(4): 373– 408. [19]. Fakhari, S., Jafarirad, A., Afzal, P. and Lotfi, M. (2019). Delineation of hydrothermal alteration Zones for porphyry systems utilizing ASTER data in Jebal barez area, SE Iran. Iranian Journal of Earth Sciences. 11: 80-92.
[20]. Zamyad, M., Afzal, P., Pourkermani, M., Nouri, R. and Jafari, M.R. (2019). Determination of Hydrothermal Alteration Zones by Remote Sensing Methods in Tirka Area, Toroud, NE Iran. Journal of the Indian Society of Remote Sensing. 47: 1817–1830.
[21]. Afzal, P., Aramesh Asl, R., Adib, A. and Yasrebi, A.B. (2015). Application of Fractal Modelling for Cu Mineralisation Reconnaissance by ASTER Multispectral and Stream Sediment Data in Khoshname Area, NW Iran Journal of the Indian Society of Remote Sensing. 43: 121-132.
[22]. Crosta, A. P., Desouza Filhi, C. R., Azevedo, F. and Brodie, C. (2003). Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, using ASTER imagery and principal component analysis. Journal of remote sensing. 10: 4233–4240. [23]. Kruse, F. A., Boardman, J. and Huntington, J. F. (2003). Comparison of airborne hyper spectral data and EO–1 Hyperion for mineral mapping. IEEE Trans. Geoscience Remote Sensing. 41: 1388–1400. [24]. Rowan, L.C. and Mars, J.C. (2003). Lithologic mapping in the Mountain Pass, California, area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Remote Sens. Environ. 84 (3): 350-366. [25]. Hubbard, B. E. and Crowley, J. K. (2005). Mineral mapping on the Chilean–Bolivian Altiplano using co–orbital ALI, ASTER and Hyperion imagery: data dimensionality issues and solutions. Remote Sensing Environment. 99: 173–186. [26]. Ducart, D. F., Crosta, A. P., Souza Filho, C. R. and Coniglio, J. (2006). Alteration mineralogy at the Cerro La Mina epithermal prospect, Patagonia, Argentina: field mapping, short–wave infrared spectroscopy, and ASTER images. Economic Geology. 101: 981–996. [27]. Mandelbrot, B.B. (1983). Fractal Geometry of Nature, San Francisco: W.H. Freeman. [28]. Dimitrijevic, M.D. (1973). Geology of the Kerman region. Report No. 52, Tehran, Iran: Ministry of Economy Geological Survey of Iran.
[29]. Khakzad, A. and Jaafari, H. (2002). Mineralogy and economic geology of copper deposits of Harare district in Kerman province. 10th Symposium of Crystallography and Mineralogy of Iran. [30]. Zarasvandi, A., Pourkaseb, H. and Jalili, Y. (2016). Investigation on the relationship between copper mineralization in Khezr Abad and Shahrebabak area: regions: Using Fractal and fry analyzes. Journal of Economic Geology. 7: 385-402. [31]. Daneshvar Saein, L. and Afzal, P. (2017). Correlation between Mo mineralization and faults using geostatistical and fractal modeling in porphyry deposits of Kerman Magmatic Belt, SE Iran. Journal of Geochemical Exploration. 181: 33-343.
[32]. Shafiei, B. (2010). Lead isotope signatures of the igneous rocks and porphyry copper deposits from the Kerman Cenozoic magmatic arc (SE Iran), and their magmatic–metallogenetic implications. Ore Geol. Rev. 38: 27–36.
[33]. Shafiei, B., Shamanian, Gh.H., Mathur, R. and Mirnejad, H. (2015). Mo isotope fractionation during hydrothermal evolution of porphyry Cu systems. Mineralium Deposita. 50: 281–291.
[34]. Alavi, M. (1994). Tectonics of Zagros orogenic belt of Iran, new data and interpretation. Tectonophysics. 229: 211–238. [35]. Gupta, R.P. (2003). Remote Sensing Geology, Berlin: Springer. [36]. Soe, M., Aung Kyaw, T. and Takashima, I. (2005). Application of remote sensing techniques on iron oxide detection from ASTER and Landsat images of tanintharyi coastal area Myanmar. Akita University. 26: 21-28. [37]. Ahmadfaraj, M., Mirmohammadi, M. and Afzal, P. (2016). Application of fractal modeling and PCA method for hydrothermal alteration mapping in the Saveh area (Central Iran) based on ASTER multispectral data. Int. J. Min. & Geo-Eng. 50(1): 37–48. [38]. Crosta, A. P. and Moore, J. M. (1989). Enhancement of Landsat Thematic Mapper imagery for residual soil mapping in SW Minais Gerais State, Brazil: a prospecting case history in Greenstone Belt Terrain. Proceedings of the Seventh Thematic Conference on Remote Sensing for Exploration Geology, 2–6 October, Calgary, Canada, ERIM, 1173–1187. [39]. Abdelkareem, M. and El-Baz, F. (2017). Characterizing hydrothermal alteration zones in Hamama area in the central Eastern Desert of Egypt by remotely sensed data. Geocarto International. 1- 20. [40]. Ruiz–Armenta, J. R. and Prol–Ledesma, R. M. (1998). Techniques for enhancing the spectral response of hydrothermal alteration minerals in Thematic Mapper images of central Mexico. International Journal of Remote Sensing. 19: 1981–2000. [41]. Beiranvand Pour, A. and Hashim, M. (2012). The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits. Journal of Ore Geology Reviews. 44: 1–9. [42]. Honarmand, M., Ranjbar, H. and Shahabpour, J. (2012). Application of Principal Component Analysis and Spectral Angle Mapper in the Mapping of Hydrothermal Alteration in the Jebal-Barez Area, Southeastern Iran. Resource Geology. 62(2): 119–139. [43]. Shahriari, H., Ranjbar, H. and Honarmand, M. (2013). Image segmentation for hydrothermal alteration mapping using PCA and concentrationarea fractal model. Natural Resources Research. 22 (3): 191–206. [44]. Cheng, Q. and Li, Q. (2002). A fractal concentration-area method for assigning a color palette for image representation. Computers and Geosciences. 28: 567–575. [45]. Carranza, E. J. M., Van Ruitenbeek, F. J. a., Hecker, C., Van der Meijde, M. and Van der Meer, F. D. (2008). Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. International Journal of Applied Earth Observation and Geoinformation. 10(3): 374–387.
[46]. Crosta, A. P., Desouza Filhi, C. R., Azevedo, F. and Brodie, C. (2003). Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, using ASTER imagery and principal component analysis. Journal of remote sensing. 24(21): 4233-4240. [47]. Carranza, E. J. M. and Hale, M. (2002). Mineral imaging with Landsat Thematic Mapper data for hydrothermal alteration mapping in heavily vegetated terrance. Journal of Remote Sensing. 23(22): .4827-4852. [48]. Honarmand,M., Ranjbar, H. and Shahabpour, J. (2011). Application of Spectral Analysis in Mapping Hydrothermal Alteration of the Northwestern Part of the Kerman Cenozoic Magmatic Arc, Iran. University of Tehran, Journal of Sciences. 22(3): 221-238.
[49]. Zoheir, B. and Emam, A. (2012). Integrating geologic and satellite imagery data for high-resolution mapping and gold exploration targets in the South Eastern Desert, Egypt. Journal of African Earth Sciences. 66-67: 22–34. [50]. Qiu, J.T., Zhang, Ch. and Hu, X. (2015). Integration of Concentration-Area Fractal Modeling and Spectral Angle Mapper for Ferric Iron Alteration Mapping and Uranium Exploration in the Xiemisitan Area, NW China. Remote Sensing. 7: 13878-13894. [51]. Parsa, M., Maghsoudi, A., Yousefi, M. and Sadeghi, M. (2016). Multifractal analysis of stream sediment geochemical data: Implications for hydrothermal nickel prospection in an arid terrain, eastern Iran. Journal of Geochemical Exploration. 181: 305-317. [52]. Rousseeuw, P.J. and Driessen, K.V. (1999). A fast algorithm for the minimum covariance determinant estimator. Technometrics. 41(3): 212-223. [53]. Parsa, M., Maghsoudi, A., Carranza, E.J.M. and Yousefi, M. (2017a). Enhancement and mapping of weak multivariate stream sediment geochemical anomalies in Ahar Area, NW Iran. Natural Resources Research. 26(4): 443-455. [54]. Buccianti, A. and Pawlowsky-Glahn, V. (2005). New Perspective on Water Chemistry and Compositional Data Analysis. Mathematical Geology. 37: 703-727, 2005. [55] Zandiyyeh, F. (2007). Mineral exploration modeling of Cu and Mo deposits using lithogeochemical data in Iran (Sungun, Darrehzar and Ijo), MSc thesis (unpublished), Shaheed Bahonar University of Kerman, Iran. [56]. Candes, E. J., Li, X., Ma, Y. and Wright, J. (2011). Robust principal component analysis. Journal of the ACM. 58(3):1–11. [57]. Cheng, Q., Agterberg, F. P. and Ballantyne, S. B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration. 51: 109-130.
[58]. Parsa, M., Maghsoudi, A., Yousefi, M. and Sadeghi, M. (2017c). Multifractal analysis of stream sediment geochemical data: Implications for hydrothermal nickel prospection in an arid terrain, eastern Iran. Journal of Geochemical Exploration. 181: 305-317. [59]. Parsa, M., Maghsoudi, A. and Yousefi, M. (2018). Spatial analyses of exploration evidence data to model skarn-type copper prospectivity in the Varzaghan district, NW Iran. Ore Geology Reviews. 92: 97-112. [60]. Parsa, M. and Maghsoudi, A. (2018). Controls on Mississippi Valley-Type Zn-Pb mineralization in Behabad district, Central Iran: Constraints from spatial and numerical analyses. Journal of African Earth Sciences. 140: 189-198. [61]. Ahmadfaraj, M., Mirmohammadi, M. and Afzal, P. (2016). Application of fractal modeling and PCA method for hydrothermal alteration mapping in the Saveh area (Central Iran) based on ASTER multispectral data. International Journal Mining and Geo-Engineering. 50: 37-48. [62]. Tripathi, V.S. (1979). Factor analysis in geochemical exploration. Journal of Geochemical Exploration. 11(3): 263-275. [63]. Jolliffe, I.T. (2002). Principal component analysis, John Wiley & Sons: Ltd. [64]. Rowan, L. C., Schmidt, R. G. and Mars, J. C. (2006). Distribution of hydrothermally altered rocks in the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTER data. Remote Sensing of Environment. 104(1): 74–87. [65]. Moore, F., Rastmanesh, F., Asadi, H. and Modabberi, S. (2008). Mapping mineralogical alteration using principal-component analysis and matched filter processing in the Takab area, north-west Iran, from ASTER data. International Journal of Remote Sensing. 29(10): 2851–2867. [66]. Hunt, G. R. (1977). Spectral signatures of particulate minerals in the visible and near infrared. Geophysics. 42(3): 501–513. [67]. Mihalasky, M.J. and Bonham-Carter, G.F. (2001). Lithodiversity and its spatial association with metallic mineral sites, Great Basin of Nevada. Journal of Natural Resources Research. 10(3): 209-226.
[68]. Yousefi, M. and Carranza, E.J.M. (2015a). Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Journal of Computers & Geosciences. 74: 97-109.
[69]. Yousefi, M. and Carranza, E.J.M. (2015b). Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Journal of Computers & Geosciences. 79: 69–81. | ||
|
آمار تعداد مشاهده مقاله: 446 تعداد دریافت فایل اصل مقاله: 683 |
||