Classification of ASTER Data by Neural Network to Mapping Alterations Related to Copper and Iron Mineralization in Birjand | ||
| Journal of Mining and Environment | ||
| مقاله 15، دوره 15، شماره 2، تیر 2024، صفحه 649-665 اصل مقاله (9.23 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2023.13640.2520 | ||
| نویسندگان | ||
| Jabar Habashi1؛ Majid Mohammady Oskouei* 1؛ Hadi Jamshid Moghadam2 | ||
| 1Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran | ||
| 2Head of the R&D department of Foladgostar Kowsar Investment Group, Iran | ||
| چکیده | ||
| The studied area located in eastern Iran shows a high potential for various mineralizations, especially copper due to its tectonic activity. Remote sensing data can effectively distinguish these areas because of the sparse vegetation. Therefore, in this study, the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multi-spectral data was used to recognize argillic, sericite, propylitic, and iron oxide alterations associated with copper mineralization. For this purpose, two categories (porphyry copper-iron and advanced argillic-iron) related alterations were considered to perform the classification of a 2617 square kilometer area using a neural network classification algorithm. To evaluate the accuracy of the classifier, the confusion matrix was computed, which provides overall accuracy and the kappa coefficient factors for assessing classification accuracy. As a result, 64.17% and 83.5% of overall accuracy, and 0.602 and 0.807 of the kappa coefficient were achieved for the advanced argillic alterations and porphyry copper categories, respectively. Ultimately, the validation of the classifications was carried out using the normalized score (NS) equation, employing quantitative criteria. Notably, the advanced argillic class emerged with the top normalized score of 2.25 out of 4, signifying a 56% alignment with the geological characteristics of the region. Consequently, this outcome has led to the identification of favorable areas in the central and northeastern parts of the studied area. | ||
| کلیدواژهها | ||
| Remote sensing؛ ASTER؛ Neural network؛ Classification؛ Normalized score | ||
| مراجع | ||
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[1]. Tagwai, M. G., Jimoh, O. A., Shehu, S. A., & Zabidi, H. (2023). Application of GIS and remote sensing in mineral exploration: current and future perspectives. World Journal of Engineering.
[2]. Schowengerdt, R. A. (2007). Remote sens: models and methods for image processing. Elsevier/Academic Press, Oxford.
[3]. Guo, H., Huang, Q., Li, X., Sun, Z., & Zhang, Y. (2013). Spatiotemporal analysis of urban environment based on the vegetation–impervious surface–soil model. Journal of Applied Remote Sensing, 8(1), 84597.
[4]. Schott, J. R. (2007). Remote sensing: the image chain approach. Oxford University Press on Demand.
[5]. Ferriere, G., & Wadge, G. (1996). The application of imaging spectrometry data to mapping alteration zones associated with gold mineralization in southern Spain. International Journal of Remote Sensing, 17(2), 331–350.
[6]. Ferrier, G., White, K., Griffiths, G., Bryant, R., & Stefouli, M. (2002). The mapping of hydrothermal alteration zones on the island of Lesvos, Greece using an integrated remote sensing dataset. International Journal of Remote Sensing, 23(2), 341–356.
[7]. Hunt, G. R., & Ashley, R. P. (1979). Spectra of altered rocks in the visible and near infrared. Economic Geology, 74(7), 1613–1629.
[8]. Abrams, M. J., & Brown, D. (1984). Silver Bell, Arizona, porphyry copper test site report: Tulsa, Oklahoma. The American Association of Petroleum Geologists, the Joint NASA–Geosat Test Case Project, Final Report, 4–73.
[9]. Abrams, M. J., Brown, D., Lepley, L., & Sadowski, R. (1983). Remote sensing for porphyry copper deposits in southern Arizona. Economic Geology, 78(4), 591–604.
[10]. Spatz, D. M., Wilson, R. T., Pierce, F. W., & Bolm, J. G. (1995). Remote sensing characteristics of porphyry copper systems, western America Cordillera. Arizona Geological Society Digest, 20, 94–108.
[11]. Lowell, J. D., & Guilbert, J. M. (1970). Lateral and vertical alteration-mineralization zoning in porphyry ore deposits. Economic Geology, 65(4), 373–408.
[12]. Abdelsalam, M. G., Stern, R. J., & Berhane, W. G. (2000). Mapping gossans in arid regions with Landsat TM and SIR-C images: the Beddaho Alteration Zone in northern Eritrea. Journal of African Earth Sciences, 30(4), 903–916.
[13]. Sabins, F. F. (1999). Remote sensing for mineral exploration. Ore Geology Reviews, 14(3–4), 157–183.
[14]. Hunt, G. R. (1977). Spectral Signatures of Particulate Minerals in The Visible and Near Infrared. GEOPHYSICS, 42(3), 501–513.
[15]. Clark, R. N., King, T. V. V, Klejwa, M., Swayze, G. A., & Vergo, N. (1990). High spectral resolution reflectance spectroscopy of minerals. Journal of Geophysical Research, 95(B8), 12653.
[16]. Crowley, J. K. (1988). Near-Infrared Reflectance Spectra of Mixtures of Kaolin-Group Minerals: Use in Clay Mineral Studies. Clays and Clay Minerals, 36(4), 310–316.
[17]. Huntington, J. F. (2007). The Role of Remote Sensing in Finding Hydrothermal Mineral Deposits on Earth. In Ciba Foundation Symposium 202‐Evolution of Hydrothermal Ecosystems on Earth (and Mars?) Evolution of Hydrothermal Ecosystems on Earth (and Mars?): Ciba Foundation Symposium 202 (pp. 214–235). Wiley Online Library.
[18]. Bedini, E., van der Meer, F., & van Ruitenbeek, F. (2009). Use of HyMap imaging spectrometer data to map mineralogy in the Rodalquilar caldera, southeast Spain. International Journal of Remote Sensing, 30(2), 327–348.
[19]. Di Tommaso, I., & Rubinstein, N. (2007). Hydrothermal alteration mapping using ASTER data in the Infiernillo porphyry deposit, Argentina. Ore Geology Reviews, 32(1–2), 275–290.
[20]. Gabr, S., Ghulam, A., & Kusky, T. (2010). Detecting areas of high-potential gold mineralization using ASTER data. Ore Geology Reviews, 38(1–2), 59–69.
[21]. Kruse, F. A., Boardman, J. W., & Huntington, J. F. (2003). Comparison of airborne hyperspectral data and eo-1 hyperion for mineral mapping. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1388–1400.
[22]. Rowan, L. C., Schmidt, R. G., & 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.
[23]. Moore, F., Rastmanesh, F., Asadi, H., & 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.
[24]. Perry, S. L. (2004). Spaceborne and airborne remote sensing systems for mineral exploration-case histories using infrared spectroscopy. Infrared Spectroscopy in Geochemistry, Exploration Geochemistry, and Remote Sensing, 33, 227–240.
[25]. Pour, B. A., Hashim, M., & Marghany, M. (2011). Using spectral mapping techniques on short wave infrared bands of ASTER remote sensing data for alteration mineral mapping in SE Iran. Int. J. Phys. Sci, 6(4), 917–929.
[26]. Tangestani, M. H., & Moore, F. (2002). Porphyry copper alteration mapping at the Meiduk area, Iran. International Journal of Remote Sensing, 23(22), 4815–4825.
[27]. Tangestani, M. H., Mazhari, N., Agar, B., & 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(10), 2833–2850.
[28]. Zhang, X., Pazner, M., & Duke, N. (2007). Lithologic and mineral information extraction for gold exploration using ASTER data in the south Chocolate Mountains (California). ISPRS Journal of Photogrammetry and Remote Sensing, 62(4), 271–282.
[29]. Abrams, M., & Yamaguchi, Y. (2019). Twenty Years of ASTER Contributions to Lithologic Mapping and Mineral Exploration. Remote Sensing, 11(11), 1394.
[30]. Richards, J. A. (2013). Feature Reduction. In Remote Sensing Digital Image Analysis (pp. 343–380). Springer Berlin Heidelberg.
[31]. Karimpour, M. H., Mazhari, N., & Shafaroudi, A. M. (2014). Discrimination of Different Erosion Levels of Porphyry Cu Deposits using ASTER Image Processing in Eastern Iran: a Case Study in the Maherabad, Shadan, and Chah Shaljami Areas. Acta Geologica Sinica - English Edition, 88(4), 1195–1213.
[32]. Mohebi, A., Mirnejad, H., Lentz, D., Behzadi, M., Dolati, A., Kani, A., & Taghizadeh, H. (2015). Controls on porphyry Cu mineralization around Hanza Mountain, south-east of Iran: An analysis of structural evolution from remote sensing, geophysical, geochemical and geological data. Ore Geology Reviews, 69, 187–198.
[33]. Sojdehee, M., Rasa, I., Nezafati, N., & Abedini, M. V. (2016). Application of spectral analysis to discriminate hydrothermal alteration zones at Daralu copper deposit, SE Iran. Arabian Journal of Geosciences, 9(1), 41.
[34]. Farahbakhsh, E., Shirmard, H., Bahroudi, A., & Eslamkish, T. (2016). Fusing ASTER and QuickBird-2 Satellite Data for Detailed Investigation of Porphyry Copper Deposits using PCA; Case Study of Naysian Deposit, Iran. Journal of the Indian Society of Remote Sensing, 44(4), 525–537.
[35]. Yousefi, S. J., Ranjbar, H., Alirezaei, S., & Dargahi, S. (2018). Discrimination of Sericite Phyllic and Quartz-Rich Phyllic Alterations by Using a Combination of ASTER TIR and SWIR Data to Explore Porphyry Cu Deposits Hosted by Granitoids, Kerman Copper Belt, Iran. Journal of the Indian Society of Remote Sensing, 46(5), 717–727. https://doi.org/10.1007/s12524-017-0745-z
[36]. Yousefi, S. J., Ranjbar, H., Alirezaei, S., Dargahi, S., & Lentz, D. R. (2018). Comparison of hydrothermal alteration patterns associated with porphyry Cu deposits hosted by granitoids and intermediate-mafic volcanic rocks, Kerman Magmatic Arc, Iran: Application of geological, mineralogical and remote sensing data. Journal of African Earth Sciences, 142, 112–123.
[37]. Pour, A. B., & Hashim, M. (2012). Identifying areas of high economic-potential copper mineralization using ASTER data in the Urumieh–Dokhtar Volcanic Belt, Iran. Advances in Space Research, 49(4), 753–769.
[38]. Honarpazhouh, J., Hassanipak, A. A., & Shabani, K. S. (2013). Integration of stream sediment geochemical and ASTER data for porphyry copper deposit exploration in Khatun Abad, North West of Iran. Archives of Mining Sciences, 58(1).
[39]. Pazand, K., Sarvestani, J. F., & Ravasan, M. R. S. (2013). Hydrothermal Alteration Mapping Using ASTER Data for Reconnaissance Porphyry Copper Mineralization in the Ahar Area, NW Iran. Journal of the Indian Society of Remote Sensing, 41(2), 379–389.
[40]. Alimohammadi, M., Alirezaei, S., & Kontak, D. J. (2015). Application of ASTER data for exploration of porphyry copper deposits: A case study of Daraloo–Sarmeshk area, southern part of the Kerman copper belt, Iran. Ore Geology Reviews, 70, 290–304.
[41]. Yazdi, Z., Jafari Rad, A. R., & Ajayebi, K. S. (2015). Analysis and modeling of geospatial datasets for porphyry copper prospectivity mapping in Chahargonbad area, Central Iran. Arabian Journal of Geosciences, 8(10), 8237–8248.
[42]. Mars, J. (2006). Regional mapping of phyllic- and argillic-altered rocks in the Zagros magmatic arc, Iran, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and logical operator algorithms. Geosphere, 2(3), 161.
[43]. Carrino, T. A., Crosta, A. P., Toledo, C. L. B., Silva, A. M., & Silva, J. L. (2015). Geology and Hydrothermal Alteration of the Chapi Chiara Prospect and Nearby Targets, Southern Peru, Using ASTER Data and Reflectance Spectroscopy. Economic Geology, 110(1), 73–90.
[44]. Rajendran, S., & Nasir, S. (2017). Characterization of ASTER spectral bands for mapping of alteration zones of volcanogenic massive sulphide deposits. Ore Geology Reviews, 88, 317–335. https://doi.org/10.1016/j.oregeorev.2017.04.016
[45]. Zhang, N., & Zhou, K. (2017). Identification of hydrothermal alteration zones of the Baogutu porphyry copper deposits in northwest China using ASTER data. Journal of Applied Remote Sensing, 11(1), 15016.
[46]. Dai, J., Qu, X., & Song, Y. (2018). Porphyry Copper Deposit Prognosis in the Middle Region of the Bangonghu-Nujiang Metallogenic Belt, Tibet, Using ASTER Remote Sensing Data. Resource Geology, 68(1).
[47]. Kurzawa, T., Bröcker, M., Rad, G. F., Berndt, J., & Lisker, F. (2017). Cretaceous high-pressure metamorphism and low pressure overprint in the Sistan Suture Zone, eastern Iran: Additional temperature estimates for eclogites, geological significance of U-Pb zircon ages and Rb-Sr constraints on the timing of exhumation. Journal of Asian Earth Sciences, 147, 332–344.
[48]. Karimpour, M. H., Zarrinkoub, M. H., & Sadeghi, M. (1996). Project of producing mineral potential map in eastern Iran». Department of Geology, University of Birjand.
[49]. Vassigh, H., & Soheili, M. (1975). Geological Map of Sar-e-chah-e-shur (1: 100000). Geological Survey of Iran (GSI).
[50]. Green, A. A., & Craig, M. D. (1985). Analysis of aircraft spectrometer data with logarithmic residuals. JPL Proc. of the Airborne Imaging Spectrometer Data Anal. Workshop.
[51]. Ghassemian, H. (2016). A review of remote sensing image fusion methods. Information Fusion, 32, 75–89.
[52]. Ibrahim, W. S., Watanabe, K., & Yonezu, K. (2016). Structural and litho-tectonic controls on Neoproterozoic base metal sulfide and gold mineralization in North Hamisana shear zone, South Eastern Desert, Egypt: The integrated field, structural, Landsat 7 ETM + and ASTER data approach. Ore Geology Reviews, 79, 62–77.
[53]. Salisbury, J. W., Hunt, G. R., & Lenhoff, C. J. (1975). Visible and near-infrared spectra: X. Stony meteorites. Modern Geology, 5, 115–126.
[54]. Kalinowski, A., & Oliver, S. (2004). ASTER Mineral Index Processing Manual, Remote Sensing Applications Geoscience Australia October 2004, pages 22,23. 22–23.
[55]. Soleimani, S. (1998). Investigating the types of gosans and their use in the exploration of mineral reserves in Kerman province (In Persian) (p. 1).
[56]. Volesky, J. C., Stern, R. J., & Johnson, P. R. (2003). Geological control of massive sulfide mineralization in the Neoproterozoic Wadi Bidah shear zone, southwestern Saudi Arabia, inferences from orbital remote sensing and field studies. Precambrian Research, 123(2–4), 235–247.
[57]. Hosseini, M. (2021). Discrimination of Hydrothermal Alterations Associated with Copper Mineralization using ASTER Image Processing Sheet in Sar-e-Chah-e-Shur, Eastern Iran (Vol. 18, Issue 1).
[58]. Oliver, S., & Kalinowski, A. (2004). ASTER Mineral Index Processing Manual. Remote Sensing Applications Geoscience Australia.
[59]. Cudahy, T. J., Okada, K., Cornelius, A., & Hewson, R. D. (2002). Regional to prospect scale exploration for porphyry-skarn-epithermal mineralisation at Yerington, Nevada, using ASTER and airborne Hyperspectral data. CSIRO Exploration and Mining Report.
[60]. Richards, J. A., & Jia, X. (1999). Remote Sensing Digital Image Analysis. Springer Berlin Heidelberg.
[61]. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
[62]. Wang, R., Bu, F., Jin, H., & Li, L. (2007). A Feature-Level Image Fusion Algorithm based on Neural Networks. 2007 1st International Conference on Bioinformatics and Biomedical Engineering, 821–824.
[63]. Haykin, S. (1998). Neural networks: a comprehensive foundation. Prentice Hall PTR.
[64]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
[65]. Zhang, X., & LeCun, Y. (2015). Text understanding from scratch. ArXiv Preprint ArXiv:1502.01710.
[66]. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.
[67]. Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, Issue 4). Springer.
[68]. Muavhi, N. (2022). Evaluation of effectiveness of supervised classification algorithms in land cover classification using ASTER images-A case study from the Mankweng (Turfloop) Area and its environs, Limpopo Province, South Africa. South African Journal of Geomatics, 9(1), 61–74.
[69]. Jensen, J. R. (1986). Introductory Digital Image Processing: A Remote Sensing Perspective. | ||
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