Advertisement

Copper potential mapping in Kerman copper bearing belt by using ANFIS method and the input evidential layer analysis

  • Mahdi Shabankareh
  • Ardeshir Hezarkhani
Original Paper

Abstract

Earth science information used in mineral potential mapping has an empirical component comprising an exploration database and a conceptual component comprising an expert knowledge base. The hybrid neuro-fuzzy model combines conceptual and empirical components of available earth science information for predictive mineral potential mapping effectively. This paper describes a neuro-fuzzy model, which combines exploration data in the regional scale for copper potential mapping in Kerman copper bearing belt in south of Iran. Data layers or evidential maps are in six datasets namely lithology, tectonic, airborne geophysics, ferric alteration, hydroxide alteration, and geochemistry. The modeling result was 1044 pixels selected as favorable in order to continue the copper exploration in the study area; in other words, approximately 11.7 % of the area was selected. Fifty six known deposits out of 86 ones, equal to 65 % of all, were located in favorable zone. Other main goals of this study were to determine how each input affects favorable output. For this purpose, the histogram of each normalized input data with its favorable output was drawn. The histograms of each input dataset for favorable output showed that each information layer has a certain behavioral pattern. These behavioral patterns can be considered as regional copper exploration criteria.

Keywords

Copper prospecting Potential mapping Hybrid neuro-fuzzy model Evidential layer analysis Behavioral pattern 

References

  1. Afzal P, Fadakar Alghalandis Y, Khakzad A, Moarefvand P, Rashidnejad Omran N (2011) Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling. J Geochem Explor 108:220–232CrossRefGoogle Scholar
  2. Agard P, Omrani J, Jolivet L, Mouthereau F (2005) Convergence history across Zagros (Iran): constraints from collisional and earlier deformation. Int J Earth Sci 94:401–419CrossRefGoogle Scholar
  3. Agterberg, F.P. (1988). Application of recent developments of regression analysis in regional mineral resource evaluation, In: Quantitative analysis of mineral and energy resources, Chung, C.F., Fabbri, G., Sinding-Larsen, R. (Ed.), 1–28, D Reidel Publishing: Dordrecht. ISBN 9027726353Google Scholar
  4. Agterberg FP, Bonham-Carter GF, Wright DF (1990) Statistical pattern integration for mineral exploration. In: Gaal G, Merriam DF (eds) Computer applications in resource estimation prediction and assessment for metals and petroleum. Pergamon Press, Oxford-New York, pp. 1–21CrossRefGoogle Scholar
  5. Agterberg, F.P., Bonham-Carter, G.F. (2005). Measuring performance of mineral-potential maps. Natural Resources Research, 14(1), 1–17, ISSN 15207439Google Scholar
  6. Alavi M (1980) Tectonostratigraphic evolution of the Zagrosides of Iran. Geology 8:144–149CrossRefGoogle Scholar
  7. An P, Moon WM, Rencz A (1991) Application of fuzzy set theory for integration of geological, geophysical and remote sensing data. Can J Explor Geophys 27:1–11Google Scholar
  8. An P, Moon WM (1993) An evidential reasoning structure for integrating geophysical, geological and remote sensing data, Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1359–1361, ISBN 0-7803-1240-6, August, 1993, TokyoGoogle Scholar
  9. Atapour H, Aftabi A (2007) The geochemistry of gossans associated with Sarcheshmeh porphyry copper deposit, Rafsanjan, Kerman, Iran: implications for exploration and the environment. J Geochem Explor 93:47–65CrossRefGoogle Scholar
  10. Behnia, P. (2007). Application of radial basis functional link networks to exploration for proterozoic mineral deposits in central Iran. Natural Resources Research, 16(2), 147–155, ISSN 15207439Google Scholar
  11. Benomar, T.B., Hu, G., Bian, F. (2009). A predictive GIS model for potential mapping of copper, lead, and zinc in langping area, China. Geo-Spatial Information Science, 12(4), 243–250, ISSN 10095020Google Scholar
  12. Berberian F, Berberian M (1981) Tectono-plutonic episodes in Iran. In: Gupta HK, Delany FM (eds) Zagroz–Hindu Kush–Himalaya Geodynamic Evolution. American Geophysical Union & Geological Society of America, Washington, pp. 5–32Google Scholar
  13. Berberian F, Muir ID, Pankhurst RJ, Berberian M (1982) Late Cretaceous and Early Miocene Andean-type plutonic activity in northern Makran and central Iran. J Geol Soc Lond 139:605–614CrossRefGoogle Scholar
  14. Bonham-Carter GF, Agterberg FP, Wright DF (1988) Integration of geological datasets for gold exploration in Nova Scotia. Photogrammetry and Remote Sensing 54(11):1585–1592Google Scholar
  15. Bonham-Carter GF, Agterberg FP, Wright DF (1989) Weights of evidence modeling: a new approach to mapping mineral potential. In: Agterberg FP, Bonham-Carter GF (eds) Statistical Applications in the Earth Sciences, Geological Survey of Canada 98. Canadian Government Publishing Centre, pp 171–183. ISBN 0660135922Google Scholar
  16. Bonham-Carter GF, Agterberg FP (1990) Application of a microcomputer based geographic information system to mineral-potential mapping. In: Hanley JT, Merriam DF (eds) Microcomputer-based applications in geology, II. Petroleum. Pergamon Press, New York, pp. 49–74CrossRefGoogle Scholar
  17. Bonham-Carter GF (1994) Geographic Information Systems for geoscientists: modeling with GIS. Pergamon Press, Ontario, 398 ppGoogle Scholar
  18. Boomeri M, Nakashima K, Lentz DR (2009) The Miduk porphyry Cu deposit, Kerman, Iran: a geochemical analysis of the potassic zone including halogen element systematics related to Cu mineralization processes. J Geochem Explor 103:17–29CrossRefGoogle Scholar
  19. Brown WM, Gedeon TD, Groves DI, Barnes RG (2000) Artificial neural networks: a new method for mineral prospectivity mapping. Aust J Earth Sci 47:757–770CrossRefGoogle Scholar
  20. Brown WM, Gedeon TD, Groves DI (2003) Use of noise to augment training data: a neural network method of mineral potential mapping in regions of limited known deposit examples. Nat Resour Res 12(3):141–152CrossRefGoogle Scholar
  21. Buckley JJ, Feuringb T (1999) Introduction to fuzzy partial differential equations. Fuzzy Sets Syst 105(2):241–248CrossRefGoogle Scholar
  22. Buragohain M, Mahanta C (2008) A novel approach for ANFIS modeling based on full factorial design. Applied Soft Computing Archive 8:609–625CrossRefGoogle Scholar
  23. Carranza EJM, Hale M (2000) Geologically constrained probabilistic mapping of gold potential, Baguio district, Philippines. Nat Resour Res 9(3):237–253CrossRefGoogle Scholar
  24. Carranza EJM (2004) Weights of evidence modeling of mineral potential: a case study using small number of prospects, Abra, Philippines. Nat Resour Res 13:173–187CrossRefGoogle Scholar
  25. Carranza, E.J.M., Woldai, T., Chikambwe, E.M. (2005). Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi District, Zambia. Natural Resources Research, 14(1), 47–63. ISSN 15207439Google Scholar
  26. Carranza EJM, van Ruitenbeek FJA, Hecker C, van der Meijde M, van der Meer FD (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 10:374–387CrossRefGoogle Scholar
  27. Chica-Olmo M, Abarca F (2002) Development of a decision support system based on remote sensing and GIS techniques for gold-rich area identification in SE Spain. Int J Remote Sens 23(22):4801–4814CrossRefGoogle Scholar
  28. Chung, C.F., Agterberg, F.P. (1980). Regression models for estimating mineral resources from geological map data. Mathematical Geology, 12(5), 473–488, ISSN 08828121Google Scholar
  29. Clark DA (1997) Magnetite petrophysics and magnetite petrology: aids to geological interpretation of magnetic surveys. AGSO Journal of Australian Geology & Geophysics 17(2):83–103Google Scholar
  30. D’Ercole, C., Groves, D.I., Knox-Robinson, C.M. (2000). Using fuzzy logic in a Geographic Information System environment to enhance conceptually based prospectively analysis of Mississippi Valley-type mineralization. Australian Journal of Earth Sciences, 47(5), 913–927, ISSN 08120099Google Scholar
  31. De Quadros, T.F.P., Koppe, J.C., Strieder, A.J., Costa, J.F.C.L. (2006). Mineral-potential mapping: A comparison of weights-of-evidence and fuzzy methods. Natural Resources Research, 15(1), 49–65, ISSN 15207439Google Scholar
  32. Eddy BG, Bonham-Carter GF, Jefferson CW (1995) Mineral resource assessment of the Parry Islands, high Arctic, Canada: A GIS-base fuzzy logic model. Proceedings of Canadian Conference on GIS, CD ROM session C3, Paper 4, OttawaGoogle Scholar
  33. Farrand WH (1997) Identification and mapping of ferric oxide and oxyhydroxide minerals in imaging spectrometer data of Summitville, Colorado, USA, and the surrounding San Juan Mountains. Int J Remote Sens 10:1543–1552CrossRefGoogle Scholar
  34. Harris DP, Pan GC (1999) Mineral favorability mapping: a comparison of artificial neural networks, logistic regression and discriminate analysis. Nat Resour Res 8(2):93–109CrossRefGoogle Scholar
  35. Harris, J.R., Lemkow, D., Jefferson, C., Wright, D., Falck, H. (2008). Mineral potential modelling for the greater Nahanni ecosystem using GIS based analytical methods. Natural Resources Research, 17(2), 51–78, ISSN 15207439Google Scholar
  36. Hassanzadeh J (1993) Metallogenic and tectonomagmatic events in the SE sector of the Cenozoic active continental margin of central Iran (Shahr e Babak area, Keman Province): Los Angeles, University of California, Ph.D. thesis, 204 pGoogle Scholar
  37. Hezarkhani A (2006a) Mineralogy and fluid inclusion investigations in the Reagan Porphyry System, Iran, the path to an uneconomic porphyry copper deposit. J Asian Earth Sci 27:598–612CrossRefGoogle Scholar
  38. Hezarkhani A (2006b) Petrology of the intrusive rocks within the sungun porphyry copper deposit, Azerbaijan, Iran. J Asian Earth Sci 27:326–340CrossRefGoogle Scholar
  39. Hezarkhani A (2006c) Mass changes during hydrothermal alteration/mineralization at the Sar-Cheshmeh porphyry copper deposit, southeastern Iran. Int Geol Rev 48:841–860CrossRefGoogle Scholar
  40. Hezarkhani A (2009) Hydrothermal fluid geochemistry at the Chah-Firuzeh porphyry copper deposit, Iran: evidence from fluid inclusions. J Geochem Explor 101:254–264CrossRefGoogle Scholar
  41. Jafari Rad AR, Busch W (2011) Porphyry copper mineral prospectivity mapping using interval valued fuzzy sets topsis method in Central Iran. J Geogr Inf Syst 3:312–317Google Scholar
  42. Jang JSR (1992) Neuro-fuzzy modeling: architecture, analyses and applications. Unpublished Ph.D Dissertation, Department of Electrical Engineering and Computer Science, University of California, Berkeley, CaliforniaGoogle Scholar
  43. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 23:665–685CrossRefGoogle Scholar
  44. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligent Prentice-Hall International, 614 ppGoogle Scholar
  45. Jianping, C., Gongwen, W., Changbo, H. (2005). Quantitative prediction and evaluation of mineral resources based on GIS: A case study in Sanjiang region, southwestern China. Natural Resources Research, 14(4), 285–294, ISSN 15207439Google Scholar
  46. Knox-Robinson CM (2000) Vectorial fuzzy logic: a novel technique for enhanced mineral prospectivity mapping with reference to the orogenic gold mineralization potential of the Kalgoorlie Terrane, Western Australia. Aust J Earth Sci 47(5):929–942CrossRefGoogle Scholar
  47. Lowell JD, Guilbert JM (1970) Lateral and vertical alteration—mineralization zoning in porphyry ore deposits. Econ Geol 65:373–408CrossRefGoogle Scholar
  48. Luo X, Dimitrakopoulos R (2003) Data-driven fuzzy analysis in quantitative mineral resource assessment. Comput Geosci 29:3–13CrossRefGoogle Scholar
  49. Mamdani EH (1974) Applications of fuzzy algorithm for control of a simple dynamic plant. Proc IEEE 121(12):1585–1588Google Scholar
  50. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1):1–13CrossRefGoogle Scholar
  51. Moon, W.M. (1990). Integration of geophysical and geological data using evidence theory function. IEEE Transactions on Geoscience and Remote Sensing, 28(4), 711–720, ISSN 0196-2892Google Scholar
  52. Moon, W.M. (1993). On mathematical representation and integration of multiple spatial geoscience data sets. Canadian Journal of Remote Sensing, 19(1), 63–67, ISSN 07038992Google Scholar
  53. Moon WM, So CS (1995) Information representation and integration of multiple sets of spatial geoscience data, International Geoscience and Remote Sensing Symposium (IGARSS), pp 2141–2144, ISBN 0-7803-2567-2, July, 1995, FirenzeGoogle Scholar
  54. Nykanen, V., Raines, G.L. (2006). Quantitative analysis of scale of aeromagnetic data raises questions about geologic-map scale. Natural Resources Research, 15(4), 213–222, ISSN 15207439Google Scholar
  55. Nykänen, V., Ojala, V.J. (2007). Spatial analysis techniques as successful mineral-potential mapping tools for orogenic gold deposits in the northern Fennoscandian shield, Finland. Natural Resources Research, 16(2), 85–92, ISSN 15207439Google Scholar
  56. Nykänen V, Groves DI, Ojala VJ, Eilu P, Gardoll SJ (2008) Reconnaissance scale conceptual fuzzy-logic prospectivity modeling for iron oxide copper-gold deposits in the northern Fennoscandian Shield, Finland. Aust J Earth Sci 55:25–38CrossRefGoogle Scholar
  57. Oh, H.J., Lee, S., 2008, Regional probabilistic and statistical mineral potential, mapping of gold–silver deposits using GIS in the Gangreung Area, Korea. Resource Geology, 58(20, 171–187Google Scholar
  58. Omrani J, Agard P, Whitechurch H, Benoit M, Prouteau G, Jolivet L (2008) Arc-magmatism and subduction history beneath the Zagros Mountains, Iran: a new report of adakites and geodynamic consequences. Lithos 106:380–398CrossRefGoogle Scholar
  59. Pan GC (1996) Extended weights of evidence modeling for the pseudo-estimation of metal grades. Nonrenewable Resources 5:53–76CrossRefGoogle Scholar
  60. 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(3), 155–177, ISSN 15207439Google Scholar
  61. Porwal A, Carranza EJM, Hale M (2004) A hybrid neuro-fuzzy model for mineral potential mapping. Math Geol 36:803–826CrossRefGoogle Scholar
  62. Porwal A, Carranza EJM, Hale M (2006) A hybrid fuzzy weights-of-evidence model for mineral potential mapping. Nat Resour Res 15:1–14CrossRefGoogle Scholar
  63. Raines, G.L. (1999). Evaluation of weights of evidence to predict epithermal-gold deposits in the Great Basin of the Western United States. Natural Resources Research, 8(4), 257–276, ISSN 15207439Google Scholar
  64. Raines, G.L., Connors, K.A., Chorlton, L.B. (2007). Porphyry copper deposit tract definition—a global analysis comparing geologic map scales. Natural Resources Research, 16(2), 191–198, ISSN 15207439Google Scholar
  65. Rencz, A.N., Harris, J.R., Watson, G.P., Murphy, B. (1994). Data integration for mineral exploration in the Antigonish Highlands, Nova Scotia: application of GIS and remote sensing. Canadian Journal of Remote Sensing, 20(3), 257–267, ISSN 07038992Google Scholar
  66. Rigol-Sanchez, J.P., Chica-Olmo, M., Abarca-Hernandez, F. (2003). Artificial neural networks as a tool for mineral potential mapping with GIS. International Journal Remote Sensing, 24(5), 1151–1156, ISSN 01431161Google Scholar
  67. Roy, R., Cassard, D., Cobbold, P.R., Rossello, E.A., Bailly, L., Lips, A.L.W. (2006). Predictive mapping for copper-gold magmatic-hydrothermal systems in NW Argentina: use of a regional-scale GIS, application of an expert-guided data-driven approach, and comparison with results from a continental-scale GIS. Ore Geology Reviews, 29(3–4), 260–286, ISSN 01691368Google Scholar
  68. Sengor, A. M. C., Altiner, D., Cin, A., Ustomer, T., Hsu, K. J. (1988), The origin and assembly of the Tethyside orogenic collage at the expense of Gondwana land. In M. G. Audley- Charles & A. Hallam (Eds.), Gondwana and Tethys. Geological Society, (pp. 119–181). London: Special Publication, Geological Society.Google Scholar
  69. Singer, D.A., Kouda, R. (1996). Application of a feed forward neural network in the search for Kuroko deposits in the Hokuroku District, Japan. Mathematical Geology, 28(8), 1017–1023, ISSN 08828121Google Scholar
  70. Singer DA, Berger VI, Moring BC (2008) Porphyry copper deposits of the world—database and grade and tonnage models, 2008: U.S. Geological Survey Open-File Report 2008–1155, 45 pGoogle Scholar
  71. Skabar, A. (2007). Modeling the spatial distribution of mineral deposits using neural networks. Natural Resource Modeling, 20(3), 435–450, ISSN 1939-7445Google Scholar
  72. Soheyli M (1981) Anar 1:250.000 Geological map, geological survey of IranGoogle Scholar
  73. Soheyli M (1985) Sirjan 1:250.000 Geological map, geological survey of IranGoogle Scholar
  74. Sugeno M, Kang GT (1988) Structure identification of fuzzy model. Fuzzy Sets Syst 28:12–33CrossRefGoogle Scholar
  75. Sugeno M, Tanaka K (1991) Successive identification of a fuzzy model and its application to prediction of complex systems. Fuzzy Sets Syst 42:315–334CrossRefGoogle Scholar
  76. Tahmasebi P, Hezarkhani A (2012) A hybrid neural networks–fuzzy logic–genetic algorithm for grade estimation. Comput Geosci 42:18–27CrossRefGoogle Scholar
  77. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modelling and control. IEEE Transactions on Systems, Man and Cybernetics 15(1):116–132CrossRefGoogle Scholar
  78. Takin M (1972) Iranian geology and continental drift in the Middle East. Nature 235:147–150CrossRefGoogle Scholar
  79. Tangestani, M.H., Moore, F. (2001). Porphyry copper potential mapping using the weights-of-evidence modeling a GIS northern Shahr-e-Babak Iran. Australian Journal of Earth Sciences, 48(5), 913–927, ISSN 08120099Google Scholar
  80. Tangestani MH, Moore F (2002a) Porphyry copper alteration mapping in the Meiduk area, Iran. Int J Remote Sens 23:4815–4825CrossRefGoogle Scholar
  81. Tangestani MH, Moore F (2002b) 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 4:65–74CrossRefGoogle Scholar
  82. Thoman, M.W., Zonge, K.L., and Liu, D., 2000, Geophysical case history of North Silver Bell, Pima County, Arizona—a supergene-enriched porphyry copper deposit. In Ellis, R.B., Irvine, R., and Fritz, F., eds., Northwest Mining Association 1998 Practical Geophysics Short Course Selected Papers on CD-ROM. Spokane, Washington, Northwest Mining Association, paper 4, 42 p.Google Scholar
  83. Titley SR, Beane RE (1981) 1981, porphyry copper deposits. In: Skinner BJ (ed) Economic Geology Seventy-fifth Anniversary Volume 1905–1980. Economic Geology Publishing Co., Littleton, pp. 214–269Google Scholar
  84. Tsukamoto Y (1979) An approach to fuzzy reasoning method. In: Gupta MM, Ragade RK, Yager RR (eds) Advances in fuzzy set theory and applications. North-Holland, Amsterdam, pp. 137–149Google Scholar
  85. Wang C, Venkatesh SS, Judd JS (1994) Optimal stopping and effective machine complexity in learning. In: Cowan JD, Tesauro G, Alspector J (eds) Advances in Neural Information Processing Systems. Morgan Kaufmann, San Francisco, pp. 303–310Google Scholar
  86. Xu, S., Cui, Z.K., Yang, X.L., Wang, G.J. (1992). A preliminary application of weights of evidence in gold exploration in Xionger mountain region. Henan province. Mathematical Geology, 24(6), 663–674, ISSN 08828121Google Scholar
  87. Ying LC, Pan MC (2008) Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Conversation and Management 49:205–211CrossRefGoogle Scholar
  88. Yousefi M, Carranza EJM (2014) Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration. Nat Resour ResGoogle Scholar
  89. Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision process. IEEE Transactions on Systems, Man and Cybernetics 3:28–44CrossRefGoogle Scholar
  90. Zohrehbakhsh A (1987) Rafsanjan1:250.000 Geological map, geological survey of Iran publication. Tehran, IranGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2016

Authors and Affiliations

  1. 1.Mining and Metallurgical Engineering DepartmentAmirkabir University of Technology (Tehran Polytechnic)TehranIran

Personalised recommendations