Mineral potential modeling using deep learning auto-encoder network in the Dehsalm district, eastern Iran

Identification of promising areas associated with mineralization and integration of exploratory multi-resource data-sets are essential in mineral potential modeling. In this research, big data analysis method and an unsupervised deep auto-encoder network algorithm were used to identify the exploratory targets areas associated with porphyry copper-gold mineralization in the Dehsalm strict of Iran. The results show that the identified exploratory target areas have strong spatial relationships with known mineral indices in the study area. The Prediction-Area (P_A) plot analysis shows that the generated model performs well. The result of this study demonstrates that big data analytics supported by deep learning methods is a potential technique to be considered for use in mineral prospectivity mapping.