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Volume 63, Issue 2
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

The aim of this work is to introduce the application of the fuzzy ordered weighted averaging method as a straightforward knowledge‐driven approach to explore porphyry copper deposits in an airborne prospect. In this paper, the proposed method is applied to airborne geophysical (potassium radiometry, magnetometry, and frequency‐domain electromagnetic) data, geological layers (fault and host rock zones), and various extracted alteration layers from remote sensing images. The central Iranian volcanic–sedimentary belt in Kerman province of Iran that is located within the Urumieh–Dokhtar (Sahand–Bazman) magmatic arc is chosen for this study. This region has high potential of mineral occurrences, especially porphyry copper, containing some active world‐class copper mines such as Sarcheshmeh. Two evidential layers, including the downward continued map and the analytic signal of such filtered magnetic data, are generated to be used as geophysical plausible traces of porphyry copper occurrences. The low values of the resistivity layer acquired from airborne frequency‐domain electromagnetic data are also used as an electrical criterion in this study. Four remote sensing evidential layers, including argillic, phyllic, propylitic, and hydroxyl alterations, are extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer images in order to map the altered areas associated with porphyry copper deposits. The Enhanced Thematic Mapper plus images are used to map iron oxide layer. Since potassium alteration is the mainstay of copper alteration, the airborne potassium radiometry data are used. Here, the fuzzy ordered weighted averaging method uses a wide range of decision strategies in order to generate numerous mineral potential/prospectivity maps. The final mineral potential map based upon desired geo‐data set indicates adequately matching of high‐potential zones with previous working mines and copper deposits.

Copyright © 2015 European Association of Geoscientists & Engineers © 2014 European Association of Geoscientists & Engineers
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2014-11-13
2024-04-19

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References

  1. AbediM., NorouziG.H. and FathianpourN.2013a. Fuzzy outranking approach: a knowledge‐driven method for mineral prospectivity mapping. International Journal of Applied Earth Observation and Geoinformation21, 556–567.
     [Google Scholar]
  2. AbediM., GholamiA. and NorouziG.H.2013b. A stable downward continuation of airborne magnetic data: a case study for mineral prospectivity mapping in Central Iran. Computers & Geosciences52, 269–280.
     [Google Scholar]
  3. AbediM., TorabiS.A. and NorouziG.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 Applicata54, 145–164.
     [Google Scholar]
  4. AbediM., NorouziG.H. and TorabiS.A.2013d. Clustering of mineral prospectivity area as an unsupervised classification approach to explore copper deposit. Arabian Journal of Geosciences6, 3601–3613.
     [Google Scholar]
  5. AbediM. and NorouziG.H.2012. Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration. Journal of Applied Geophysics83, 35–45.
     [Google Scholar]
  6. AbediM., TorabiS.A., NorouziG.H., HamzehM. and ElyasiG.R.2012a. PROMETHEE II: A knowledge‐driven method for copper exploration. Computers & Geosciences46, 255–263.
     [Google Scholar]
  7. AbediM., NorouziG.H. and BahroudiA.2012b. Support vector machine for multi‐classification of mineral prospectivity areas. Computers & Geosciences46, 272–283.
     [Google Scholar]
  8. AbediM., TorabiS.A., NorouziG.H. and HamzehM.2012c. ELECTRE III: A knowledge‐driven method for integration of geophysical data with geological and geochemical data in mineral prospectivity mapping. Journal of Applied Geophysics87, 9–18.
     [Google Scholar]
  9. AgardP., OmraniJ., JolivetL. and MouthereauF.2005. Convergence history across Zagros (Iran): constraints from collisional and earlier deformation. International Journal of Earth Sciences94, 401–419.
     [Google Scholar]
  10. AgterbergF.P. and Bonham‐CarterG.F.1999. Logistic regression and weights of evidence modeling in mineral exploration. Proceedings of the 28th International Symposium on Applications of Computer in the Mineral Industry (APCOM), Golden, Colorado, 483–490.
  11. AhmadT. and Posht KuhiM.1993. Geochemisty and petrogenesis of Urumiah–Dokhtar volcanics around Nain and Rafsanjan areas: a preliminary study. Treatise on the Geology of Iran, Iranian Ministry of Mines and Metals, 90 pp.
     [Google Scholar]
  12. AnP., MoonW.M. and RenczA.N.1991. Application of fuzzy theory for integration of geological, geophysical and remotely sensed data: Can. Journal of Exploration Geophysics27, 1–11.
     [Google Scholar]
  13. AnsariA.H. and AlamdarK.2009. Reduction to the Pole of Magnetic Anomalies Using Analytic Signal. World Applied Sciences Journal7, 405–409.
     [Google Scholar]
  14. AraújoC.C. and MacedoA.B.2002. Multicriteria geologic data analysis for mineral favorability mapping: application to a metal sulphide mineralized area, Ribeira Valley Metallogenic Province Brazil. Natural Resources Research11, 29–43.
     [Google Scholar]
  15. AtapourH. and AftabiA.2007. The geochemistry of gossans associated with Sarcheshmeh porphyry copper deposit, Rafsanjan, Kerman, Iran: Implications for exploration and the environment. Journal of Geochemical Exploration93, 47–65.
     [Google Scholar]
  16. BerberianF. and BerberianM.1981. Tectono‐plutonic episodes in Iran. In: Zagros–Hindu Kush–Himalaya Geodynamic Evolution (eds H.K.Gupta and F.M.Delany ), pp. 5–32. American Geophysical Union & Geological Society of America, Washington.
     [Google Scholar]
  17. Bonham‐CarterG.F.1994. Geographic Information Systems for Geoscientists: Modelling with GIS. Pergamon Press, Oxford, 398 pp.
     [Google Scholar]
  18. Bonham‐CarterG.F., AgterbergF.P. and WrightD.F.1989. Weights‐of‐evidence modelling: a new approach to mapping mineral potential. In: Statistical Applications in the Earth Sciences, Paper 89–9 (eds F.P.Agterberg and G.F.Bonham‐Carter ), pp. 171–183. Geological Survey of Canada.
     [Google Scholar]
  19. CalijuriM.L., MarquesE.T., LorentzJ.F., AzevedoR.F. and CarvalhoC.A.B.2004. Multi‐criteria analysis for the identification of waste disposal areas. Geotechnical and Geological Engineering22(2), 299–312.
     [Google Scholar]
  20. CarranzaE.J.M.2004. Weights of evidence modeling of mineral potential: a case study using small number of prospects, Abra, Philippines. Natural Resources Research13, 173–187.
     [Google Scholar]
  21. CarranzaE.J.M.2008. Geochemical anomaly and mineral prospectivity mapping in GIS. Handbook of Exploration and Environmental Geochemistry, Vol. 11, 351 pp. Elsevier, Amsterdam.
     [Google Scholar]
  22. CarranzaE.J.M.2010. Improved wildcat modelling of mineral prospectivity. Resource Geology60, 129–149.
     [Google Scholar]
  23. CarranzaE.J.M. and HaleM.2000. Geologically constrained probabilistic mapping of gold potential, Baguio district, Philippines. Natural Resources Research9, 237–253.
     [Google Scholar]
  24. CarranzaE.J.M. and HaleM.2001a. Geologically constrained fuzzy mapping of gold mineralization potential, Baguio district, Philippines. Natural Resources Research10, 125–136.
     [Google Scholar]
  25. CarranzaE.J.M. and HaleM.2001b. Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines. Exploration and Mining Geology10, 165–175.
     [Google Scholar]
  26. CarranzaE.J.M. and HaleM.2002a. Evidential belief functions for data‐driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geology Reviews22, 117–132.
     [Google Scholar]
  27. CarranzaE.J.M. and HaleM.2002b. Mineral imaging with Landsat Thematic Mapper data for hydrothermal alteration mapping in heavily vegetated terrane. International Journal of Remote Sensing23, 4827–4852.
     [Google Scholar]
  28. CarranzaE.J.M. and HaleM.2002c. Spatial association of mineral occurrences and curvilinear geological features. Mathematical Geology34, 203–221.
     [Google Scholar]
  29. CarranzaE.J.M. and HaleM.2002d. Where are porphyry copper deposits spatially localized? A case study in Benguet Province, Philippines. Natural Resources Research11, 45–59.
     [Google Scholar]
  30. CarranzaE.J.M. and HaleM.2002e. Wildcat mapping of gold potential, Baguio district, Philippines. Transactions Institute of Mining and Metallurgy (Applied Earth Science)111, 100–105.
     [Google Scholar]
  31. CarranzaE.J.M., HaleM. and FaassenC.2008a. Selection of coherent deposit‐type locations and their application in data‐driven mineral prospectivity mapping. Ore Geology Reviews33, 536–558.
     [Google Scholar]
  32. CarranzaE.J.M., van RuitenbeekF.J.A., HeckerC.A., van der MeijdeM. and van der MeerF.D.2008b. Knowledge‐guided data‐driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. International Journal of Applied Earth Observation and Geoinformation10, 374–387.
     [Google Scholar]
  33. CarranzaE.J.M., MangaoangJ.C. and HaleM.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 Research8, 165–173.
     [Google Scholar]
  34. CarranzaE.J.M., WoldaiT. and ChikambweE.M.2005. Application of data – driven evidential belief functions to prospectivity mapping for aquamarine – bearing pegmatites, Lundazi district, Zambia. Natural Resources Research14, 47–63.
     [Google Scholar]
  35. ChiclanaF., Herrera‐ViedmaE., HerreraF. and AlonsoS.2007. Some induced ordered weighted averaging operators and their use for solving group decision‐making problems based on fuzzy reference relations. European Journal of Operational Research182, 383–399.
     [Google Scholar]
  36. ChungC.F. and MoonW.M.1990. Combination rules of spatial geoscience data for mineral exploration. Geoinformatics2, 159–169.
     [Google Scholar]
  37. DağdevirenM.2008. Decision making in equipment selection: an integrated approach with AHP and PROMETHEE. Journal of Intelligent Manufacturing19, 397–406.
     [Google Scholar]
  38. DaneshfarB.1997. An evaluation of indicators of prospectivity and potential mapping of porphyry deposits in middle and southern British Columbia by a GIS study of regional geochemical and other geoscientific data. PhD thesis, University of Ottawa, Canada.
  39. DercourtJ., RicouL.E. and VrielynckB.1993. Atlas Tethys Palaeoenvironmental maps, 14 maps, 1 pl. Gauthier‐Villars, Paris.
  40. DercourtJ., ZonenshainL.P., RicouL.E., KazminV.G., Le PichonX., KnipperA.L., et al. 1986. Geological evolution of the Tethys belt from the Atlantic to the Pamirs since the Lias. Tectonophysics123, 241–315.
     [Google Scholar]
  41. DrobneS. and LisecA.2009. Multi‐attribute Decision Analysis in GIS: Weighted Linear Combination and Ordered Weighted Averaging. Informatica33, 459–474.
     [Google Scholar]
  42. FullerR.1996. OWA operators in decision making. In: Exploring the Limits of Support Systems, Vol. 3 (ed C.Carlsoon ), pp. 85–104. TUCS General Publications, Turku Centre for Computer Science, Abo Akademi University, Turkey.
     [Google Scholar]
  43. HassanzadehJ.1993. Metallogenic and Tectonomagmatic Events in the SE Sector of the Cenozoic Active Continental Margin of Central Iran . University of California, Los Angeles, 1–204.
     [Google Scholar]
  44. JiangH. and EastmanJ.R.2000. Application of fuzzy measures in multi‐criteria evaluation in GIS. International Journal of Geographical Information Systems14, 173–184.
     [Google Scholar]
  45. MalczewskiJ.2006. Ordered weighted averaging with fuzzy quantifiers: GIS‐based multicriteria evaluation for land‐use suitability analysis. International Journal of Applied Earth Observation and Geoinformation8, 270–277.
     [Google Scholar]
  46. MalczewskiJ., ChapmanT., FlegelC., WaltersD., ShrubsoleD. and HealyM.A.2003. GIS‐multicriteria evaluation with ordered weighted averaging (OWA): case study of developing watershed management strategies. Environment and Planning A35(10), 1769–1784.
     [Google Scholar]
  47. MakropoulosC. and ButlerD.2005. Spatial ordered weighted averaging: incorporating spatially variable attitude towards risk in spatial multi‐criteria decision‐making. Environmental Modelling & Software21(1), 69–84.
     [Google Scholar]
  48. MakropoulosC., ButlerD. and MaksimovicC.2003. A fuzzy logic spatial decision support system for urban water management. Journal of Water Resources Planning and Management129(1), 69–77.
     [Google Scholar]
  49. MendesJ.F.G. and MotizukiW.S.2001. Urban quality of life evaluation scenarios: The case of São Carlos in Brazil. CTBUH Review1(2), 1–10.
     [Google Scholar]
  50. MoonW.M.1990. Integration of geophysical and geological data using evidential belief function. IEEE Transactions on Geoscience and Remote Sensing28, 711–720.
     [Google Scholar]
  51. MoradianA.1997. Geochemistry, geochronology and petrography of feldspathoid bearing rocks in Urumieh‐Dokhtar volcanic belt, Iran . Unpublished PhD thesis, University of Wollongong, Australia, 412 pp.
     [Google Scholar]
  52. NabighianM.N.1984. Toward a three‐dimensional automatic interpretation of potential field data via generalized Hilbert transforms: Fundamental relations. Geophysics49, 780–786.
     [Google Scholar]
  53. NabighianM.N.1974. Additional comments on the analytic signal of two dimensional magnetic bodies with polygonal cross‐section. Geophysics39, 85–92.
     [Google Scholar]
  54. NabighianM.N.1972. The analytic signal of two‐dimensional magnetic bodies with polygonal cross‐section: its properties and use for automated anomaly interpretation. Geophysics37, 507–517.
     [Google Scholar]
  55. NadiS. and DelavarM.R.2011. Multi‐criteria, personalized route planning using quantifier‐guided ordered weighted averaging operators. International Journal of Applied Earth Observation and Geoinformation13, 322–335.
     [Google Scholar]
  56. NykänenV. and SalmirinneH.2007. Prospectivity Analysis of Gold Using Regional Geophysical and Geochemical Data from the Central Lapland Greenstone Belt, Finland. Geological Survey of Finland, 251–269.
     [Google Scholar]
  57. OmraniJ., AgardP., WhitechurchH., BenoitM., ProuteauG. and JolivetL.2008. Arc‐magmatism and subduction history beneath the Zagros Mountains, Iran: A new report of adakites and geodynamic consequences. Lithos106, 380–398.
     [Google Scholar]
  58. PanG. and HarrisD.P.2000. Information Synthesis for Mineral Exploration. Oxford University Press, New York, pp. 461.
     [Google Scholar]
  59. PazandK., HezarkhaniA. and AtaeiM.2012. Using TOPSIS approaches for predictive porphyry Cu potential mapping: A case study in Ahar‐Arasbaran (NW‐Iran). Computers & Geosciences49, 62–71.
     [Google Scholar]
  60. PorwalA., CarranzaE.J.M. and HaleM.2003a. Artificial neural networks for mineral‐potential mapping: a case study from Aravalli Province, Western India. Natural Resources Research12, 156–171.
     [Google Scholar]
  61. PorwalA., CarranzaE.J.M. and HaleM.2003b. Knowledge‐driven and data‐driven fuzzy models for predictive mineral potential mapping. Natural Resources Research12, 1–25.
     [Google Scholar]
  62. PorwalA., CarranzaE.J.M. and HaleM. 2004. A hybrid neuro‐fuzzy model for mineral potential mapping. Mathematical Geology36, 803–826.
     [Google Scholar]
  63. PorwalA., CarranzaE.J.M. and HaleM.2006a. Bayesian network classifiers for mineral potential mapping. Computers & Geosciences32, 1–16.
     [Google Scholar]
  64. PorwalA., CarranzaE.J.M. and HaleM.2006b. A hybrid fuzzy weights‐of‐evidence model for mineral potential mapping. Natural Resources Research15, 1–15.
     [Google Scholar]
  65. RanjbarH., MasoumiF. and CarranzaE.J.M.2011. Evaluation of geophysics and spaceborne multispectral data for alteration mapping in the Sar Cheshmeh mining area, Iran. International Journal of Remote Sensing32, 3309–3327.
     [Google Scholar]
  66. RanjbarH. and HonarmandM.2004. Integration and analysis of airborne geophysical and ETM+ data for exploration of porphyry type deposits in the Central Iranian Volcanic Belt using fuzzy classification. International Journal of Remote Sensing25, 4729–4741.
     [Google Scholar]
  67. RashedT. and WeeksJ.2003. Assessing vulnerability to earthquake hazards through spatial multicriteria analysis of urban areas. International Journal of Geographical Information Science17(6), 547–576.
     [Google Scholar]
  68. RasmussenB.M., MelgaardB. and KristensenB.2001. GIS for decision support—designation of potential wetlands. In: The Third International Conference on Geospatial Information in Agriculture and Forestry, Denver, USA.
     [Google Scholar]
  69. RicouL.E. and Braudj BrunnjH.1977. Le Zagros. MEmoire Hors‐SErie N° 8 de la SociEtE GEologique de France8, 33–52.
     [Google Scholar]
  70. RinnerC. and MalczewskiJ.2002. Web‐enabled spatial decision analysis using ordered weighted averaging. Journal of Geographical Systems4(4), 385–403.
     [Google Scholar]
  71. RoweG. and WrightG.2001. Expert Opinions in Forecasting: The role of the Delphi Technique. In: Principles of Forecasting (ed J.Armstrong ), pp. 125–144. Kluwer Academic, Boston.
     [Google Scholar]
  72. SengorA.M.C., AltinerD., CinA., UstomerT. and HsuK.J.1988. The origin and assembly of the Tethyside orogenic collage at the expense of Gondwana land. In: Gondwana and Tethys (eds M.G.Audley‐Charles and A.Hallam ), pp. 119–181. Geological Society Special Publication, Geological Society.
     [Google Scholar]
  73. SiemonB.2001. Improved and new resistivity‐depth profiles for helicopter electromagnetic data. Journal of Applied Geophysics46, 65–76.
     [Google Scholar]
  74. SingerD.A. and KoudaR.1996. Application of a feed forward neural network in the search for Kuroko deposits in the Hokuroku district, Japan. Mathematical Geology28, 1017–1023.
     [Google Scholar]
  75. TakinM.1972. Iranian geology and continental drift in the Middle East. Nature235, 147–150.
     [Google Scholar]
  76. Teresa LamataM. 2004. Ranking of Alternatives with Ordered Weighted Averaging Operators. International Journal of Intelligent Systems19, 473–482.
     [Google Scholar]
  77. XianS.2012. Fuzzy Linguistic Induced Ordered Weighted Averaging Operator and Its Application. Journal of Applied Mathematics2012, 1–10.
     [Google Scholar]
  78. WatermanG.C. and HamiltonR.L.1975. The Sar Cheshmeh porphyry copper deposit. Economic Geology70, 568–576.
     [Google Scholar]
  79. YagerR.R.1988. On ordered weighted averaging aggregation operators in multi‐criteria decision making. IEEE Transactions on Systems, Man, and Cybernetics18, 183–190.
     [Google Scholar]
  80. YagerR.R.1996. Quantifier guided aggregation using OWA operators. International Journal of Intelligent Systems11, 49–73.
     [Google Scholar]
  81. ZadehL.A.1983. A computational approach to fuzzy quantifiers in natural languages. Computers & Mathematics with Applications9, 149–184.
     [Google Scholar]
  82. ZuoR. and CarranzaE.J.M.2011. Support vector machine: A tool for mapping mineral prospectivity. Computer & Geosciences37, 1967–1975.
     [Google Scholar]
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Mineral potential mapping in Central Iran using fuzzy ordered weighted averaging method
Geophysical Prospecting 63, 461 (2015); https://doi.org/10.1111/1365-2478.12186
/content/journals/10.1111/1365-2478.12186
/content/journals/10.1111/1365-2478.12186
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  • Article Type: Research Article
Keyword(s): Airborne geophysical layers; Fuzzy ordered weighted averaging; Geological layers; Mineral potential/prospectivity mapping; Porphyry copper deposit; Remote sensing layers

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