GIS-Based Identification of Promising Porphyry Copper Mineralization Areas in Shahre Babak (Kerman Province, Iran) using Machine Learning Method

Producing mineral potential model using GIS software has been increased over the past years. In this study, predictive map consisted of argillic alteration, philic alteration, iron oxide alteration, reduction to pole of aeromagnetic data, lineaments, cu geochemistry anomaly, and principal component analysis (component 3) were prepared from Shahre Babak area. For training model, 37 mineralized points were used. Point pattern analysis was used as well for making non-deposit points and for training model, percepteron artificial neural network with two layers was applied. The training model was used to prepare the final mineral potential model. Based on the mentioned model, the main promising areas were identified to be in the northwest and eastern part of the studied area. Moreover, two areas in the northern and southwestern parts of this area were identified for additional studies. For evaluating the model, ROC curve was used. ROC curve shows high precision of the produced model. For more evaluating, sensitivity, specificity, positive predict value, negative predict value, accuracy, and kappa were computed. The coefficients confirm the high accuracy of the mineral potential model.