Comparison of various knowledge-driven and logistic-based mineral prospectivity methods to generate Cu and Au exploration targets Case study: Feyz-Abad area (North of Lut block, NE Iran)
Comparison of various knowledge-driven and logistic-based mineral prospectivity methods to generate Cu and Au exploration targets Case study: Feyz-Abad area (North of Lut block, NE Iran)
عنوان فارسی :
Comparison of various knowledge-driven and logistic-based mineral prospectivity methods to generate Cu and Au exploration targets Case study: Feyz-Abad area (North of Lut block, NE Iran)
عنوان انگلیسی :
Comparison of various knowledge-driven and logistic-based mineral prospectivity methods to generate Cu and Au exploration targets Case study: Feyz-Abad area (North of Lut block, NE Iran)
چکیده انگلیسی:
Motivated by the recent successful results of using GIS modeling in a variety of problems related to the geosciences, some knowledge-based methods were applied to a regional scale mapping of the mineral potential, special for Cu-Au mineralization in the Feyz-Abad area located in the NE of Iran. Mineral Prospectivity Mapping (MPM) is a multi-step process that ranks a promising target area for more exploration. In this work, five integration methods were compared consisting of fuzzy, continuous fuzzy, index overlay, AHP, and fuzzy AHP. For this purpose, geological maps, geochemical samples, and geophysics data were collected, and a spatial database was constructed. ETM + images were used to extract the hydroxyl and iron-oxide alterations, and to identify the linear and fault structures and prospective zones in regional scale; ASTER images were used to extract SiO2 index, kaolinite, chlorite, and propylitic alterations in a district scale. All the geological, geochemical, and geophysical data was integrated for MPM by different analysis. The values were determined by expert knowledge or logistic functions. Based upon this analysis, three main exploration targets were recognized in the Feyz-Abad district. Based on field observation, MPM was proved to be valid. The prediction result is accurate, and can provide directions for future prospecting. Among all the methods evaluated in this work, which tend to generate relatively similar results, the continuous fuzzy model seems to be the best fit in the studied area because it is bias-free and can be used to generate reliable target areas.
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