A clustering approach for mineral potential mapping: A deposit-scale porphyry copper exploration targeting

نوع مقاله : مقاله پژوهشی


Abedi, M., Mostafavi Kashani, S.B., Norouzi, G.H., Yousefi, M., 2017. A deposit scale mineral prospectivity analysis: A comparison of various knowledge–driven approaches for porphyry copper targeting in Seridune, Iran. Journal of African Earth Sciences, 128: 127–146.##
Abedi, M., Norouzi, G.H., Fathianpour, N., 2015. Fuzzy ordered weighted averaging method: a knowledge–driven approach for mineral potential mapping. Geophys Prospect., 63: 46–477.##
Abedi, M., Norouzi, G.H., Fathianpour, N., 2013a. Fuzzy outranking approach: a knowledge–driven method for mineral prospectivity mapping. International Journal of Applied Earth Observation and Geoinformation, 21: 556–567.##
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Abedi, M., Norouzi, G.H., Torabi, S.A., 2013b. Clustering of mineral prospectivity area as an unsupervised classification approach to explore copper deposit. Arabian Journal of Geosciences, 6: 3601–3613.##
Abedi, M., Torabi, S., Norouzi, G.H., 2013c. Application of fuzzy AHP method to integrate geophysical data in a prospect scale, a case study: Seridune copper deposit. Bollettino di Geofisica Teorica ed Applicata, 54: 145–164.##
Abedi, M., Norouzi, G.H., 2012. Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration. Journal of Applied Geophysics, 83: 35–45.##
Abedi, M., Torabi, S.A., Norouzi, G.H., Hamzeh, M., 2012a. ELECTRE III: A knowledge–driven method for integration of geophysical data with geological and geochemical data in mineral prospectivity mapping. Journal of Applied Geophysics, 87: 9–18.##
Abedi, M., Torabi, S.A., Norouzi, G.H., Hamzeh, M., Elyasi, G.R., 2012b. PROMETHEE II: a knowledge–driven method for copper exploration. Computers & Geosciences, 46: 255–263.##
Agterberg, F., Bonham–Carter, G.F., 1999. Logistic regression and weights of evidence modeling in mineral exploration: Proceedings Proceedings of the 28th International Symposium on Applications of Computer in the Mineral Industry (APCOM), 483: 490.##
Agterberg, F., Bonham–Carter, G.F., Wright, D., 1990. Statistical pattern integration for mineral exploration, Computer applications in resource estimation. Elsevier, 1–21.##
An, P., Moon, W., Rencz, A., 1991. Application of fuzzy set theory for integration of geological, geophysical and remote sensing data. Canadian Journal of Exploration Geophysics, 27 (1): 1–11.##
Barak, S., Abedi, M., Bahroudi, A., 2019. A knowledge–guided fuzzy inference approach for integrating geophysics, geochemistry and geology data in deposit–scale porphyry copper targeting, Saveh–Iran. Bollettino di Geofisica Teorica e Applicata (in press).##
Berberian, M., King, G., 1981. Towards a paleogeography and tectonic evolution of Iran. Canadian Journal of Earth Sciences, 18 (2): 210–265.##
Bonham–Carter, G.F., 1994. Geographic information systems for geoscientists–modeling with GIS. Computer methods in the geoscientists, 13: p. 398.##
Bonham–Carter, G.F., 1989. Weights of evidence modeling: a new approach to mapping mineral potential. Statistical Applications in the Earth Sciences, 171–183.##
Carranza, E.J.M., 2008. Geochemical anomaly and mineral prospectivity mapping in GIS, Elsevier, p. 368.##
Carranza, E.J.M., Hale, M., 2002a. Spatial association of mineral occurrences and curvilinear geological features. Mathematical Geology, 34 (2): 203–221.##
Carranza, E.J.M., Hale, M., 2002b. Where are porphyry copper deposits spatially localized? A case study in Benguet province, Philippines. Natural Resources Research, 11 (1): 45–59.##
Carranza, E.J.M., Hale, M., 2001. Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines. Exploration and Mining Geology, 10 (3): 165–175.##
Carranza, E.J.M., Mangaoang, J.C., Hale, M., 1999. Application of mineral exploration models and GIS to generate mineral potential maps as input for optimum land–use planning in the Philippines. Natural Resources Research, 8 (2):165–173.##
Chung, C.J.F., Moon, W.M., 1991. Combination rules of spatial geoscience data for mineral exploration. Geoinformatics, 2 (2): 159–169.##
Clark, D.A., 1999. Magnetic petrology of igneous intrusions: implications for exploration and magnetic interpretation. Exploration Geophysics, 30 (2): 5–26.##
Dehghan Nayeri, R., 2018. Porphyry copper potential mapping in Narbaghi through TOPSIS multi–criteria decision making method: MSc. Thesis in University of Tehran, Iran (published in Persian).##
Eberle, D.G., Paasche, H., 2012. Integrated data analysis for mineral exploration: A case study of clustering satellite imagery, airborne gamma–ray, and regional geochemical data suites. Geophysics, 77 (4): B167–B176.##
Ghalamghash, J., Fenodi, M., 1998. Geological map of Saveh Quadrangle (scale 1: 100000). Geological survey of Iran.##
Harris, D., Zurcher, L., Stanley, M., Marlow, J., Pan, G., 2003. A comparative analysis of favorability mappings by weights of evidence, probabilistic neural networks, discriminant analysis, and logistic regression. Natural Resources Research, 12 (4), 241–255.##
John, D., Ayuso, R., Barton, M., Blakely, R., Bodnar, R., Dilles, J., Gray, F., Graybeal, F., Mars, J., McPhee, D., 2010. Porphyry copper deposit model, Chapter B of Mineral deposit models for resource assessment: US Geological Survey Scientific Investigations Report 2010–5070–B.##
Kashani, S.B.M., Abedi, M., Norouzi, G.H., 2016. Fuzzy logic mineral potential mapping for copper exploration using multi–disciplinary geo–datasets, a case study in seridune deposit, Iran. Earth Science Informatics, 9 (2): 167–181.##
Kazemi, K., Kananian, A., Xiao, Y., Sarjoughian, F., 2019. Petrogenesis of Middle–Eocene granitoids and their Mafic microgranular enclaves in central Urmia–Dokhtar Magmatic Arc (Iran): evidence for interaction between felsic and mafic magmas. Geoscience Frontiers, 10 (2): 705–723.##

Mejía–Herrera, P., Royer, J.J., Caumon, G., Cheilletz, A., 2015. Curvature attribute from surface–restoration as predictor variable in Kupferschiefer copper potentials. Natural Resources Research, 24 (3): 275–290.##
Mirzaei, M., Afzal, P., Adib, A., Khalajmasoumi, M., Zarifi, A.Z., 2014. Prospection of iron and manganese using index overlay. and fuzzy logic methods in balvard 1: 100,000 sheet, southeastern Iran. Iran J Earth Sci., 6: 1–11.##
Moon, W.M., 1990. Integration of geophysical and geological data using evidential belief function. IEEE Transactions on Geoscience and Remote Sensing, 28 (4): 711–720.##
Moradi, M., Basiri, S., Kananian, A., Kabiri, K., 2015. Fuzzy logic modeling for hydrothermal gold mineralization mapping using geochemical, geological, ASTER imageries and other geo–data, a case study in Central Alborz, Iran. Earth Science Informatics, 8 (1): 197–205.##
Najafi, A., Karimpour, M.H., Ghaderi, M., 2014. Application of fuzzy AHP method to IOCG prospectivity mapping: A case study in Taherabad prospecting area, eastern Iran. International Journal of Applied Earth Observation and Geoinformation, 33: 142–154.##
Nykänen, V., 2008. Radial basis functional link nets used as a prospectivity mapping tool for orogenic gold deposits within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield. Natural Resources Research, 17 (1): 29–48.##
Nykänen, V., Salmirinne, H., 2007. Prospectivity analysis of gold using regional geophysical and geochemical data from the Central Lapland Greenstone Belt, Finland. Geological Survey of Finland, 44: 251–269.##
Paasche, H., Eberle, D.G., 2009. Rapid integration of large airborne geophysical data suites using a fuzzy partitioning cluster algorithm: a tool for geological mapping and mineral exploration targeting. Exploration Geophysics, 40 (3):277–287.##
Pan, G., Harris, D.P., 2000. Information synthesis for mineral exploration (spatial information systems). Oxford University Press, p. 460.##
Pazand, K., Hezarkhani, A., 2015. Porphyry Cu potential area selection using the combine AHP–TOPSIS methods: a case study in Siahrud area (NW, Iran). Earth Science Informatics, 8 (1): 207–220.##
Porwal, A., Carranza, E., Hale, M., 2003. Artificial neural networks for mineral–potential mapping: a case study from Aravalli Province, Western India. Natural resources research, 12 (3): 155–171.##
Porwal, A., Carranza, E., Hale, M., 2004. A hybrid neuro–fuzzy model for mineral potential mapping. Mathematical Geology, 36 (7): 803–826.##
Rajabinasab, B., Asghari, O., 2019. Geometallurgical Domaining by Cluster Analysis: Iron Ore Deposit Case Study. Natural Resources Research, 28 (3): 665–684.##
Ramazi, H., Jalali, M., 2015. Contribution of geophysical inversion theory and geostatistical simulation to determine geoelectrical anomalies. Studia Geophysica et Geodaetica, 59 (1): 97–112.##
Rezaei, S., Lotfi, M., Afzal, P., Jafari, M.R., Meigoony, M.S., 2015. Delineation of Cu prospects utilizing multifractal modeling and stepwise factor analysis in Noubaran 1: 100,000 sheet, Center of Iran. Arabian Journal of Geosciences, 8 (9): 7343–7357.##
Sadeghi, B., Khalajmasoumi, M., 2015. A futuristic review for evaluation of geothermal potentials using fuzzy logic and binary index overlay in GIS environment. Renewable and Sustainable Energy Reviews, 43: 818–831.##
Sadeghi, B., Khalajmasoumi, M., Afzal, P., Moarefvand, P., 2014. Discrimination of iron high potential zones at the zaghia iron ore deposit, bafq, using index overlay GIS method. Iran J Earth Sci., 6: 91–98.##
Shabankareh, M., Hezarkhani, A., 2017. Application of support vector machines for copper potential mapping in Kerman region, Iran. Journal of African Earth Sciences, 128: 116–126.##
Shahabpour, J., 2005. Tectonic evolution of the orogenic belt in the region located between Kerman and Neyriz. Journal of Asian Earth Sciences, 24 (4): 405–417.##
Singer, D.A., Kouda, R., 1996. Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district, Japan. Mathematical Geology, 28 (8): 1017–1023.##
Tangestani, M.H., Moore, F., 2002. The use of Dempster–Shafer model and GIS in integration of geoscientific data for porphyry copper potential mapping, north of Shahr–e–Babak, Iran. International Journal of Applied Earth Observation and Geoinformation, 4 (1): 65–74.##
Thoman, M.W., Zonge, K.L., Liu, D., 1998. Geophysical case history of North Silver Bell, Pima County, Arizona–a supergene–enriched porphyry copper deposit. Northwest Mining Association, p. 42.##
Yousef, M., Kreuzer, O.P., Nykänen, V., Hronsky, J.M.A., 2019. Exploration information systems – A proposal for the future use of GIS in mineral exploration targeting. Ore Geology Reviews, 111: 103005.##
Yousefi, M., Carranza, E.J.M., 2016a. Data–driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration. Natural Resources Research, 25 (1): 3–18.##
Yousefi, M., Carranza, E.J.M., 2016b. Union score and fuzzy logic mineral prospectivity mapping using discretized and continuous spatial evidence values. J. Afr. Earth Sci., 128: 47–60.##
Yousefi, M., Carranza, E.J.M., 2015a. Fuzzification of continuous–value spatial evidence for mineral prospectivity A clustering approach for mineral potential mapping: A deposit-scale … 163 mapping. Computers & Geosciences, 74: 97–109.##
Yousefi, M., Carranza, E.J.M., 2015b. Geometric average of spatial evidence data layers: a GIS–based multi–criteria decision–making approach to mineral prospectivity mapping. Computers & Geosciences, 83: 72–79.##
Yousefi, M., Carranza, E.J.M., 2015c. Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences, 79: 69–81.##
Zhang, Z., Zuo, R., Xiong, Y., 2016. A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn–type Fe deposits in the southwestern Fujian metallogenic belt, China. Science China Earth Sciences, 59 (3): 556–572.##