Advertisement

A weighted fuzzy aggregation GIS model in the integration of geophysical data with geochemical and geological data for Pb–Zn exploration in Takab area, NW Iran

  • M. Farzamian
  • A. Kamkar Rouhani
  • A. Yarmohammadi
  • H. Shahi
  • H. A. Faraji Sabokbar
  • M. Ziaiie
Original Paper

Abstract

Detailed geophysical and geochemical surveys were carried out to determine Pb–Zn mineralization zones in Chichakloo area, east of Takab, Iran. Resistivity and induced polarization (IP) surveys were conducted along 10 parallel profiles on the dolomite unit, and also 292 samples were collected for lithogeochemical studies to assess the extents of Pb–Zn ore deposits in the study area. All exploration data were processed and modeled, and then the results were taken to a geographic information system (GIS) environment to generate a mineral potential map of the area to suggest more accurate or less risky exploration drilling targets. A fuzzy logic approach was used in this study to integrate exploration predictor maps. A new approach was used for fuzzification of the geochemical maps based on the geochemical mineralization probability index (GMPI) calculation, and an approach was proposed to infer a geophysical predictor map from three-dimensional (3D) IP and resistivity maps. Furthermore, the weighted Yager t-norm fuzzy operator was applied for the integration of exploration predictor maps to consider the importance of each map in the mineral potential map generation. The mineral potential map indicates a remarkable overlapping of geophysical and geochemical anomalies in the south of the study area with a north–south trend. The results of drilling boreholes in the area confirm the obtained mineral exploration results.

Keywords

Pb–Zn mineralization Resistivity IP Geochemical surveys GIS Fuzzy logic 

Notes

Acknowledgments

Financial assistance provided by Shahrood University of Technology, Iran, is greatly appreciated. The authors also need to thank Karim Karam-Soltani and Amir Emam-Jomeh for their suggestions and supply of information to this research work. The advice and assistance of Mahyar Yousefi and Ramin Hendi are gratefully acknowledged. We thank the reviewers for their constructive comments that helped us improve this paper.

References

  1. Alavi M (1994) Tectonics of the Zagros orogenic belt of Iran: new data and interpretation. Tectonophysics 229:211–238CrossRefGoogle Scholar
  2. An P, Moon WM, Rencz AN (1991) Application of fuzzy theory for integration of geological, geophysical and remotely sensed data. Can J Explor Geophys 27(1):1–11Google Scholar
  3. An P, Moon WM, Bonham-Carter GF (1994a) An object-oriented knowledge representation structure for exploration data integration. Nonrenewablen Resour 3:132–145CrossRefGoogle Scholar
  4. An P, Moon WM, Bonham-Carter GF (1994b) Uncertainty management in integration of exploration data using the belief function. Nonrenewable Resour 3:60–71CrossRefGoogle Scholar
  5. Agterberg FP (1992) Combining indicator patterns in weights of evidence modeling for resource evaluation. Nonrenew Resour 1(1):35–50CrossRefGoogle Scholar
  6. Agterberg FP (2011) A modified weights-of-evidence method for regional mineral resource estimation. Nat Resour Res 20:95–101CrossRefGoogle Scholar
  7. Bonham-Carter GF (1994) Geographic information systems for geoscientists: modelling with GIS. Pergamon Press, New York, 398 pGoogle Scholar
  8. Bonham-Carter GF, Agterberg FP, Wright DF (1988) Integration of geological datasets for gold exploration in Nova Scotia. Photogramm Eng Remote Sens 54(11):1585–1592Google Scholar
  9. Brown WM, Gedeon TD, Groves DI, Barnes RG (2000) Artificial neural networks: a new method for mineral prospectivity mapping. Aust J Earth Sci 47(4):757–770CrossRefGoogle Scholar
  10. Carranza EJM, Hale M, Mangaoang JC (1999) Application of mineral exploration models and GIS to generate mineral potential maps as input for optimum land-use planning in the Philippines. Nat Resour Res 8(2):165–173CrossRefGoogle Scholar
  11. Carranza EJM, Hale M (2002) Wildcat mapping of gold potential, Baguio district, Philippines. Trans Inst Min Metall 111:100–105Google Scholar
  12. Carranza EJM (2004) Weights-of-evidence modelling of mineral potential: a case study using small number of prospects, Abra. Philippines Nat Resour Res 13:173–187CrossRefGoogle Scholar
  13. Carranza EJM, Woldai T, Chikambwe EM (2005) Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi district. Zambia Nat Resour Res 14:47–63CrossRefGoogle Scholar
  14. 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 J Appl Earth Obs Geoinf 10:374–387CrossRefGoogle Scholar
  15. Carranza EJM (2010) Improved wildcat modelling of mineral prospectivity. Resour Geol 60:129–149CrossRefGoogle Scholar
  16. Carranza EJM (2011) Geocomputation of mineral exploration targets. Comput Geosci 37:1907–1916CrossRefGoogle Scholar
  17. Cheng Q, Agterberg FP (1999) Fuzzy weights of evidence and its application in mineral potential mapping. Nat Resour Res 8:27–35CrossRefGoogle Scholar
  18. Cheng Q, Jing L, Panahi A (2006) Principal component analysis with optimum order sample correlation coefficient for image enhancement. Int J Remote Sens 27(16):3387–3401CrossRefGoogle Scholar
  19. Cheng Q, Bonham-Carter G, Wang W, Zhang S, Li W, Xia Q (2011) A spatially weighted principal component analysis for multi-element geochemical data for mapping locations of felsic intrusions in the Gejiu mineral district of Yunnan. China Comput Geosci 37:662–669CrossRefGoogle Scholar
  20. Chung CF, Agterberg FP (1980) Regression models for estimating mineral resources from geological map data. Math Geology 12(5):473–488CrossRefGoogle Scholar
  21. Davis JC (2002) Statistics and data analysis in geology, 3rd edn. Wiley, New York, 550 ppGoogle Scholar
  22. Dubois D, Prade H (1985) A review of fuzzy set aggregation connectives. Inf Sci 36:85–121CrossRefGoogle Scholar
  23. Eddy, B. G., Bonham-Carter, G. F., Jefferson, C. W., 1995. Mineral resource assessment of the Parry Islands, high Arctic, Canada: a GIS-based fuzzy logic model. In: Proc. Can. Conf. on GIS, CD ROM Session C3, Can. Ins. Geomatics, Ottawa, Canada, Paper 4.Google Scholar
  24. Fallon M, Porwal A, Guj P (2010) Prospectivity analysis of the Plutonic Marymia Greenstone Belt. Western Australia Ore Geol Rev 38:208–218CrossRefGoogle Scholar
  25. Ford A, Blenkinsop TG (2008) Combining fractal analysis of mineral deposit clustering with weights of evidence to evaluate patterns of mineralization: application to copper deposits of the Mount Isa Inlier, NW Queensland. Australia Ore Geol Rev 33:435–450CrossRefGoogle Scholar
  26. Ford A, Hart CJ (2013) Mineral potential mapping in frontier regions: a Mongolian case study. Ore Geol Rev 51:15–26CrossRefGoogle Scholar
  27. Fung CC, Iyer V, Brown W, Wong KW (2005) Comparing the performance of different neural networks architectures for the prediction of mineral prospectivity. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics. Guangzhou., pp 394–398Google Scholar
  28. Gilg HA, Boni M, Balassone G, Allen CR, Banks D, Moore F (2005) Marble-hosted sulfide ores in the Anguran Zn-(Pb-Ag) deposit, NW Iran: interaction of sedimentary brines with a metamorphic core complex. Mineral Deposita 41:1–16CrossRefGoogle Scholar
  29. González-Álvarez I, Porwal A, Beresford SW, McCuaig TC, Maier WD (2010) Hydrothermal Ni prospectivity analysis of Tasmania. Australia Ore Geol Rev 38:168–183CrossRefGoogle Scholar
  30. Hamdi B (1995) Precambrian–Cambrian deposits in Iran. In: Hushmandzadeh A (ed) Treatise of the geology of Iran, vol 20. Geological Survey of Iran, Tehran, 535p Google Scholar
  31. Harris JR, Wilkinson L, Heather K, Fumerton S, Bernier MA, Ayer J, Dahn R (2001) Application of GIS processing techniques for producing mineral prospectivity maps—a case study: mesothermal Au in the Swayze Greenstone Belt, Ontario. Canada Nat Resour Res 10:91–124CrossRefGoogle Scholar
  32. Harris DP, Zurcher L, Stanley M, Marlow J, Pan G (2003) A comparative analysis of favourability mappings by weights of evidence, probabilistic neural networks, discriminant analysis, and logistic regression. Nat Resour Res 12:241–255CrossRefGoogle Scholar
  33. Harris JR, Sanborn-Barrie M, Panagapko DA, Skulski T, Parker JR (2006) Gold prospectivity maps of the Red Lake greenstone belt: application of GIS technology. Can J Earth Sci 43:865–893CrossRefGoogle Scholar
  34. Joly A, Porwal A, McCuaig TC (2012) Exploration targeting for orogenic gold deposits in the Granites–Tanami Orogen: mineral system analysis, targeting model and prospectivity analysis. Ore Geol Rev 48:349–383CrossRefGoogle Scholar
  35. Karam-Soltani K (1997) Report on exploration operations for lead and zinc in Chichakloo area, Iranian Ministry of Industries and Mines (in Persian)Google Scholar
  36. Kaymak U (1998) Fuzzy decision making with control applications. PhD Thesis, Delft University of Technology, Delft, The NetherlandsGoogle Scholar
  37. Kaymak, U., Sousa, J.M., 2003. Weighted constraint aggregation in fuzzy optimisation. Kluwer Academic Publishers, NetherlandsGoogle Scholar
  38. Leach, D.L., Sangster, D.F., Kelley, K.D., Large, R.R., Garven, G., Allen, C.R., Gutzmer, J., Walters, S. (2005) Sediment-hosted lead-zinc deposits: a global perspective. Economic Geology, 100th Anniversary Volume, Lancaster, PA, p561-607Google Scholar
  39. Lisitsin VA, González-Álvarez I, Porwal A (2013) Regional prospectivity analysis for hydrothermal-remobilised nickel mineral systems in western Victoria. Australia Ore Geol Rev 52:100–112CrossRefGoogle Scholar
  40. Loke, M. H., 2001. Tutorial: 2-D and 3-D electrical imaging surveys. Course Notes for USGS Workshop “2-D and 3-D Inversion and Modeling of Surface and Borehole Resistivity Data”, Storrs, CTGoogle Scholar
  41. Loughlin WP (1991) Principal component analysis for alteration mapping. Photogramm Eng Remote Sens 57(9):1163–1169Google Scholar
  42. Lusty PAJ, Scheib C, Gunn AG, Walker ASD (2012) Reconnaissance-scale prospectivity analysis for gold mineralisation in the Southern Uplands-Down-Longford Terrane, Northern Ireland. Nat Resour Res 21:359–382CrossRefGoogle Scholar
  43. McCammon RB (1973) Nonlinear regression for dependent variables. Math Geol 5:365–375CrossRefGoogle Scholar
  44. Moon WM (1990) Integration of geophysical and geological data using evidential belief function. IEEE Trans Geosci Remote Sens 28:711–720CrossRefGoogle Scholar
  45. Moon WM (1993) On mathematical representation and integration of multiple geoscience data sets. Can J Remote Sens 19:663–667CrossRefGoogle Scholar
  46. Nykanen 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. Nat Resour Res 17:29–48CrossRefGoogle Scholar
  47. Porwal A, Das RD, Chaudhary B, Gonzalez-Alvarez I, Kreuzer O (2014) Fuzzy inference systems for prospectivity modeling of mineral systems and a case-study for prospectivity mapping of surficial uranium in Yeelirrie area. Ore Geol. Rev, Western AustraliaGoogle Scholar
  48. Porwal A, Kreuzer OP (2010) Introduction to the special issue: mineral prospectivity analysis and quantitative resource estimation. Ore Geol Rev 38(3):121–127CrossRefGoogle Scholar
  49. Porwal A, Carranza EJM, Hale M (2006) Bayesian network classifiers for mineral potential mapping. Comput Geosci 32(1):1–16CrossRefGoogle Scholar
  50. Porwal A, Carranza EJM, Hale M (2004) A hybrid neuro-fuzzy model for mineral potential mapping. Math Geol 36:803–826CrossRefGoogle Scholar
  51. Porwal A, Carranza EJM, Hale M (2003a) Artificial neural networks for mineral potential mapping. Nat Resour Res 12:155–171Google Scholar
  52. Porwal A, Carranza EJM, Hale M (2003b) Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping. Nat Resour Res 12(1):1–25CrossRefGoogle Scholar
  53. Robinson VB (2003) A perspective on the fundamentals of fuzzy sets and their use in geographic information systems transactions in GIS 73–30Google Scholar
  54. Shahi H, Ghavami R, Kamkar Rouhani K, Asadi-Haroni H (2014) Identification of mineralization features and deep geochemical anomalies using a new FT-PCA approach. J Geopersia 4(2):101–110Google Scholar
  55. Sinclair AJ, Woodsworth GJ (1970) Multiple regression as a method of estimating exploration potential in an area near Terrace, B.C. Econ Geol 65(8):998–1003CrossRefGoogle Scholar
  56. Singer DA, Kouda R (1996) Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku District, Japan. Math Geol 28(8):1017–1023CrossRefGoogle Scholar
  57. Singer DA, Kouda R (1997) Classification of mineral deposits into types using mineralogy with a probabilistic neural network. Nonrenewable Resour 6(1):27–32CrossRefGoogle Scholar
  58. Sousa, J.M., Kaymak, U., 2002. Fuzzy decision making in modeling and control. World ScientificGoogle Scholar
  59. Stockli DF, Hassanzadeh J, Stockli LD, Axen GJ, Walker JD, Dewane TJ (2004) Structural and geochronological evidence for Oligo-Miocene intra-arc low-angle detachment faulting in the Takab–Zanjan area, NW Iran. Abstr Programs Geol Soc Am 36(5):319Google Scholar
  60. Tangestani MH, Moore F (2001) Porphyry copper potential mapping using the weights-of-evidence model in a GIS, northern Shahr-e-Babak, Iran. Aust J Earth Sci 48:695–701CrossRefGoogle Scholar
  61. Tangestani MH, Moore F (2003) Mapping porphyry copper potential with a fuzzy model, northern Shahr-e-Babak, Iran. Aust J Earth Sci 50(3):311–317CrossRefGoogle Scholar
  62. Yager RR (1980) On a general class of fuzzy connectives. Fuzzy Sets Syst 4:235–242CrossRefGoogle Scholar
  63. Yousefi M, Kamkar-Rouhani A, Carranza EJM (2012) Geochemical mineralization probability index (GMPI): a new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping. J Geochem Explor 115:24–35CrossRefGoogle Scholar
  64. Kamkar-Rouhani M, Yousefi M, Carranza EJ (2013) Weighted drainage catchment basin mapping of stream sediment geochemical anomalies for mineral potential mapping. J Geochem Explor 128:88–96CrossRefGoogle Scholar
  65. Yousefi M, Kamkar-Rouhani A, Carranza EJM (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(1):45–58Google Scholar
  66. Yousefi, M., Carranza, E. J. M., 2014. Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration. Nat. Resour. Res. doi:  10.1007/s11053-014-9261-9
  67. Yousefi M, Carranza EJM (2015) Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Comput Geosci 74:97–109CrossRefGoogle Scholar
  68. Zadeh LA (1965) Fuzzy sets. IEEE Information and Control 8(3):338–353CrossRefGoogle Scholar
  69. Zadeh, L. A., 1973. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on System, Man and Cybernetics, SMC3, 28–44Google Scholar
  70. Zimmermann HJ (1991) Fuzzy set theory and its application, 2nd edn. Kluwer Academic Publishers, BostonCrossRefGoogle Scholar
  71. Zimmermann HJ, Zysno P (1980) Latent connectives in human decision making. Fuzzy Sets Syst 4:37–51CrossRefGoogle Scholar
  72. Zuo R (2011) Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum—area fractal modeling in the Gangdese Belt, Tibet (China). J Geochem Explor 111:13–22CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2016

Authors and Affiliations

  • M. Farzamian
    • 1
  • A. Kamkar Rouhani
    • 2
  • A. Yarmohammadi
    • 3
  • H. Shahi
    • 4
  • H. A. Faraji Sabokbar
    • 5
  • M. Ziaiie
    • 2
  1. 1.Centro de GeofísicaUniversidade de LisboaLisbonPortugal
  2. 2.Faculty of Mining and GeophysicsShahrood University of TechnologyShahroodIran
  3. 3.Tarbiat Modares UniversityTehranIran
  4. 4.Department of Mining EngineeringUniversity of GonabadGonabadIran
  5. 5.Faculty of GeographyTehran UniversityTehranIran

Personalised recommendations