A Combination of Data Driven and Knowledge Driven ofOutranking MCDM methods in Porphyry Cu prospectivityMapping

Due to the diverse mineral resources in Iran, mineral exploration is critical to discovering newmineralization areas. Therefore, in this study, an efficient outranking method to integrate evidential layers andmineral potential/prospectivity mapping (MPM) in Chaharginbad, one of the potential areas for porphyrycopper mineralization, has been studied. In recent years, a combination of knowledge-driven and data-drivenmethods has been successfully used in mineral exploration and MPM. In the context of exploration,geodatabase of geology, remote sensing, geochemical and geophysical data can be applied as essential data inthe preparation of target mineralization evidential layers, and known mineral occurrences can be used asdefinitive data in the evaluation and validation of obtained results. In this study, we improve and evaluate theMOORA and TOPSIS MCDM methods for MPM and outranking mineralization areas to consider uncertaintiesof the evidential layers and the available data. For this purpose, the fractal method of concentration-area (C-A)and prediction-area (P-A) plots and normalized density as authentic methods are conducted for classifying,weighting, and integrating evidential layers and evaluation of final maps. MPM models obtained in this studyof the target area to identify porphyry copper mineralization areas showed valid results so that the process andalgorithms implemented in this study can be applied to the other type of mineralization, other regions, andfurther mineral exploration studies.

A Combination of Data Driven and Knowledge Driven ofOutranking MCDM methods in Porphyry Cu prospectivityMapping Keywords:

Chahargonbad , Evidential layers , MOORA , MPM , Outranking , Porphyry Cu ,