Mineral Prospectivity Modeling with Airborne Geophysics and Geochemistry Data: a Case Study of Shahr-e-Babak Studied Area, Southern Iran

The present paper gives out data-driven method with airborne magnetic data, airborne radiometric data, and geochemistry data. The purpose of this study is to create a mineral potential model of the Shahr-e-Babak studied area. The studied area is located in the south-eastern of Iran. The various evidential layers include airborne magnetic data, airborne radiometric data (potassium and thorium), lineament density map, cu geochemistry signature, and multi-variate geochemistry signature (PC1). High magnetic anomalies, lineament structures, and alteration zones (K/Th) were derived from airborne geophysics data. Geochemistry signatures (Cu and PC1) were derived from stream sediment data. The principal Component Analysis (PCA) as an unsupervised machine learning method and five evidential layers were used to produce a porphyry prospectivity model. As a result of this combination, mineral prospectivity model was produced. Then a plot of cumulative percent of the studied area versus pca prospectivity value was used to discrete high potential areas. Then to evaluate the ability of this MPM, the location of known cu indications was used. The results confirm an acceptable outcome for porphyry prospectivity modeling. Based on this model highpotential areas are located in south southwestern and eastern parts of the studied area.