Prospectivity mapping of Iron oxide-Copper-Gold (IOCG) deposits using support vector machine method in Feyzaabad area (east of Iran)
Keywords:
Mineral prospectivity mapping, SVM method, IOCG, Feyzaabad.Abstract
Feyzaabad area is situated in the northeastern part of Iran that hosts mainly Iron Oxide Copper-Gold (IOCG) mineralization. In the present study, support vector machine (SVM), as a supervised classification method in mineral prospectivity mapping, is applied in 1:100000 Feyzaabad area, in east of Iran. Different evidential layers such as hydrothermal alteration, geological and geochemical data were integrated to generate prospectivity model for IOCG mineralization. The outcomes of the SVM method show that prospective target areas for IOCG deposits are defined mainly by vicinity to NE–SW trending faults and pyroclastic rocks (mainly tuff) and Au-Cu geochemical anomalies. These outcomes show that SVM is a potentially effective method in order to integrate multiple information evidence layers in predictive mapping of mineral prospectivity. The final prospectivity model investigation demonstrate that beside identifying known IOCG deposits, which were applied as training regions in the applied method to evaluate the SVM, the applied method has specified some new targets as well. So the target areas shown in the final prospectivity model can be applied for follow-up exploration of the IOCG deposits.
References
Abedi, M., Nourouzi, Gh., Bahroudi, A., 2012. Support vector machine for multi- classification of mineral prospectivity areas, Computers & Geosciences, 46, 272-283.
Agterberg, F.P., Bonham-Carter, G.F., 1999. Logistic regression and weights of evidence modeling in mineral exploration. In: Proceedings of the 28th Inter-national Symposium on Applications of Computer in the Mineral Industry (APCOM), Golden, Colorado, pp. 483–490.
Aizerman, Mark A.; Braverman, Emmanuel M.; and Rozonoer, Lev, I., 1964. Theoretical foundations of the potential function method in pattern recognition learning “Automation and Remote Control, 25, 821–837.
Ali, S.S., Nizamuddin, S., Abdulraheem, A., Hassan, Md.R., Hossain, M.E., 2013. Hydraulic unit prediction using support vector machine, Journal of Petroleum Science and Engineering, 110, 243-252.
An, P., Moon, W.M., Bonham-Carter, G.F., 1992. On knowledge-based approach on integrating remote sensing, geophysical and geological information. In: Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), 1992, pp. 34–38.
Barton, M.D., Johnson, D.A., 2004. Footprints of Fe-oxide (-Cu–Au) systems. SEG, 2004. Predictive Mineral Discovery Under Cover. Centre for Global Metallogeny, Spec. Pub. 33, The University of Western Australia, pp. 112–116.
Bonham-Carter, G.F., 1994, Geographic Information Systems for Geoscientists: Modelling with GIS. Pergamon, Ontario, 398pp.
Bonham-Carter, G.F., Agterberg, F.P., Wright, D. F., 1989. Weights of evidence modelling: a new approach to mapping mineral potential. In: Agterberg, F.P., Bonham-Carter, G.F. (Eds.), Statistical Applications in the Earth Sciences. Geological Survey of Canada, Paper 89-9, pp. 171–183.
Boser, B. E.; Guyon, I. M.; Vapnik, V. N., 1992. A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory-COLT' 92. p. 144. doi: 10.1145/130385.130401.ISBN089791497X.
Carranza, E.J.M., Hale, M., 2002. Wildcat mapping of gold potential, Baguio district, Philippines. Transactions of the Institution of Mining and Metallurgy (Section B-Applied Earth Science) 111, 100–105.
Carranza, E.J.M., Laborte, A.G., 2014. Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm, Ore Geology Reviews, http://dx.doi.org/10.1016/j.oregeorev.2014.08.010.
Carranza, E.J.M., 2008. Geochemical anomaly and mineral prospectivity mapping in GIS. In: Handbook of Exploration and Environmental Geochemistry, vol. 11, Elsevier, Amsterdam, 351 pp.
Cortes, C., Vapnik, V., 1995. Support-vector networks, Machine Learning, 20(3), 273-297.
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J., 2008. LIBLINEAR: A library for large linear classification, Journal of Machine Learning Research, 9, 1871–1874.
Heydari, A., 2011, Exploration report of Kuh Zar deposit in Torbat-e-Heydarieh area, Zarmehr mining company, 133p.
Hitzman, M.W., 2000. Iron oxide–Cu–Au deposits: what, where, when, and why. In: Porter, T.M. (Ed.), Hydrothermal Iron Oxide Copper-gold & Related Deposits: A Global Perspective, vol. 2. PGC Publishing, Adelaide, Australia, pp. 9–25.
Kavzoglu, T., Colkesen, I., 2009. A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation 11, 352–359.
Lusty, P.A.J., Scheib, C., Gunn, A.G., Walker, A.S.D., 2012. Reconnaissance-scale prospectivity analysis for gold mineralisation in the southern Uplands-Down-Longford Terrane, Northern Ireland. Nat. Resour. Res. 21, 359–382.
Macharis, C., Springael, J., Brucker, K.D., Verbeke, A., 2004. PROMETHEE and AHP: the design of operational synergies in multi criteria analysis. Strengthening PROMETHEE with ideas of AHP. Eur. J. Oper. Res. 153, 307–317.
Mejía-Herrera, P., Royer, J.J., Caumon, G., Cheilletz, A., 2014. Curvature attribute from surface restoration as predictor variable in Kupferschiefer copper potentials. Natural Resources, Res. http://dx.doi.org/10.1007/s11053-014-9247-7.
Moon, W.M., 1990. Integration of geophysical and geological data using evidential belief function. IEEE Trans. Geosci. Remote Sens. 28, 711–720.
Najafi,A., Karimpour, M.H., Ghaderi, M., 2014. Application of fuzzy AHP method to IOCG prospectivity mapping: Acase study in Taherabad prospecting area, eastern Iran, International Journal of Applied Earth Observation and Geoinformation, 33, 142–154.
Otukei, J.R., Blaschke, T., 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms, International Journal of Applied Earth Observation and Geoinformation, 12(1), 527-531.
Pan, G., Harris, D.P., 2000. Information Synthesis for Mineral Exploration. Oxford Univ. Press, New York, 461 pp.
Peng, L., Niu, R., Huang, B., Wu, X., Zhao, Y., Ye, R., 2014. Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China, Geomorphology, 204, 287-301.
Peng, X., Wang, Y., Xu, D., 2013. Structural twin parametric-margin support vector machine for binary classification, Knowledge-Based Systems, 49, ISSN 09507051,http://dx.doi.org/10.1016/j.kn osys.2013.04.01 3, pages 6372.
Pollard, P.J., 2000. Evidence of a magmatic fluid and metal source for Fe-oxide Cu–Aumineralisation. In: Porter, T.M. (Ed.), Hydrothermal Iron Oxide Copper- Gold &Related Deposits: A Global Perspective, vol. 1. Australian Mineral Foundation, Adelaide, Australia, pp. 27–46.
Porwal, A., Carranza, E.J.M., Hale, M., 2003. Artificial neural networks for mineral-potential mapping: a case study from Aravalli Province, Western India. Natural Resources Research, 12, 156–171.
Porwal, A., Carranza, E.J.M., Hale, M., 2006. A hybrid fuzzy weights-of-evidence model for mineral potential mapping. Natural Resources Research, 15, 1–14.
Pradhan, B., 2013. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS, Computers & Geosciences, 51, 350-365.
Rao, T., Rajinikanth, T. V., Supervised Classification of Remote Sensed Data Using Support Vector Machine. Global Journal of Computer Science and Technology: Software & Data Engineering, 14(1), 71-76.
Rodriguez-Gallano, V., Sanchez-Castillo, M., Chica-Olmo, M., Chica-Rivas, M., 2015. Machine learning predictive models for mineral prospectivity, An evaluation of neural networks, random forest, regression trees and support vector machines, Ore Geology Reviews, doi:10.1016/j.oregeorev.2015.01.001.
Shafapour Tehrany, M., Pradhan, B., Mansor, S., Ahmad, N., 2015. Flood susceptibility assessment using GIS- based support vector machine model with different kernel types, CATENA, 125, 91-101.
Shafapour Tehrany, M., Pradhan, B., Neamah Jebur, M., 2014. Flood susceptibility mapping using a novel ensemble weights- of-evidence and support vector machine models in GIS, Journal of Hydrology, 512, 332-343.
Singer, D.A., Kouda, R., 1996. Application of a feedforward neural network in the search for Kuruko deposits in the Hokuroku district, Japan. Mathematical Geology, 28, 1017–1023.
Vapnik, V., 1995. Nature of Statistical Learning Theory. John Wiley and Sons, Inc., New York.
Williams, P.J., Barton, M.D., Johnson, D.A., Fontbote, L., de Haller, A., Mark, G., Oliver,N.H.S., Marschik, R., 2005. Iron oxide copper gold deposits; geology, space-time distribution, and possible modes of origin. Economic Geology, 100, 371–406.
William H.; Teukolsky, Saul A.; Vetterling, William T.; Flannery, B. P., 2007, Section 1 6, 5. Support Vector Machines". Numerical Recipes: The Art of Scientific Computing (3rd ed.). New York: Cambridge University Press.
Yao, X., Tham, L.G., Dai, F.C., 2008. Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China, Geomorphology, 101(4), 572-582.
Yousefi, M., Carranza, E.J. M., 2015. Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling, Computers and Geosciences, 79, 69-81.
Yousefi, M., Kamkar-Rouhani, A., Carranza, E.J.M., 2012. Geochemical mineralization probability index (GMPI): a new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success inmineral potential mapping. J. Geochem. Explor. 115, 24–35.
Yousefi, M., Kamkar-Rouhani, A., Carranza, E.J.M., 2014. Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping. Geochem.: Explor. Environ. Anal. 14, 45–58.
Yu, L., Porwal, A., Holden, E.J., Dentith, M., 2012. Towards automatic lithological classification from remote sensing data using support vector machines, Computers & Geosciences, 45, 229-239.
Zuo, R., Carranza, E.J.M., 2011. Support vector machine: A tool for mapping mineral prospectivity. Computers & Geosciences, 37, 1967-1975.