Document Type : Research Paper
School of Mining Engineering, College of Engineering, University of Tehran, Iran
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
Department of Mining Engineering, University of Tehran
In integrating geospatial datasets for mineral potential mapping (MPM), the uncertainty model of MPM can be inferred from the Dempster – Shafer rules of combination. In addition to generating the uncertainty model, evidential belief functions (EBFs) present the belief, plausibility and disbelief of MPM, whereby four models can be simultaneously utilized to facilitate the interpretation of mineral favourability output. To investigate the functionality and applicability of the EBFs, we selected the Naysian porphyry copper district located on the Urmia – Dokhtar magmatic belt in the northeast of the Isfahan city, central Iran. Multidisciplinary datasets- that are geochemical and geophysical data, ASTER satellite images, Quickbird and ground survey- were designed in a geospatial database to run MPM. Implementing the Dempster law through intersection (And) and union (OR) operators led to different MPM performances. To amplify the accuracy of the generated favourability maps, a combinatory EBFs technique was applied in three ways: (1) just OR operator, (2) just And operator, and (3) combination of And and OR operators. The plausibility map (as mineral favourability map) was compared to Cu productivity values derived from drilled boreholes, where the MPM accuracy of hybrid method was higher than each individual operator. Of note the success rate of hybrid method validated by 21 boreholes was about 84%, and it demarcates high favourability zones occupying 0.67 km2 of the studied area.