A combinative knowledge-driven integration method for integrating geophysical layers with geological and geochemical datasets

Abstract

In this research, we establish the ability of some famous Multi Criteria Decision Making (MCDM) techniques which are called “The technique for order preference by similarity to ideal solution” (TOPSIS) and evidential belief functions (EBFs) in deposit scale studies. Zafarqand copper‑molybdenum deposit (located in centeral region of the Urumieh-Dokhtar Volcanic Arc (UDVA) in Iran) was chosen as case study for this research. Various evidential layers (geophysical, geological and geochemical) were usually raster-based maps which were integrated to attain mineral prospectivity mapping (MPM) by variants of TOPSIS. In this research, TOPSIS variants were completer each other in locations of mineralization zones applying EBFs method with Dempster-Shafer's rules. EBFs method can avoid of losing high value pixels in final produced MPM and clear uncertainty degree. Field checking and microscopic studies of borehole's samples verified high confidence level of final MPM (up to 93%) and percent of missed high values pixels of TOPSIS variants (up to 40%). Accordingly, it is better that knowledge-driven integration methods not be separately executed in deposit scale studies.