Bashir Saljoughi - Department of Mining and Metallurgy Engineering, Amirkabir University of technology (Tehran Polytechnic), Tehran, Iran
Ardeshir Hezarkhani - Department of Mining and Metallurgy Engineering, Amirkabir University of technology (Tehran Polytechnic), Tehran, Iran

The porphyry Cu-mineralization potential area studied in this article is located in the southern section of the Central Iranian volcano–sedimentary complex, contains large number of mineral deposits, and occurrences that are currently facing a shortage of resources. Therefore, prospecting potential areas in the deeper and peripheral spaces has become a high priority in this region. Different direct and indirect methods try to predict promising areas for future explorations that most of them are very time-consuming and costly. The main goal of mineral prospecting is applying a transparent and robust approach for identifying high potential areas to be explored further in the future. This study presents the procedure taken to create two different Cu-mineralization prospectivity maps. This study aims to investigate the results of applying the ANN technique, and to compare them with the outputs of applying GEP method. The geo-datasets employed for creating evidential maps of porphyry Cu mineralization include solid geology map, alteration map, faults, dykes, airborne total magnetic intensity, airborne gamma-ray spectrometry data (U, Th, K and total count), and known Cu occurrences. Based on this study, the ANN technique (۱۰ neurons in the hidden layer and LM learning algorithm) is a better predictor of Cu mineralization compared to the GEP method. The results obtained from the P-A plot showed that the ANN model indicates that ۸۰% (vs. ۷۰% for GEP) of the identified copper occurrences are projected to be present in only ۲۰% (vs. ۳۰% for GEP) of the surveyed area. The ANN technique due to capabilities such as classification, pattern matching, optimization, and prediction is useful in identifying anomalies associated with the Cu mineralization.

Mineral Prospectivity Mapping, Artificial Neural Network, Gene expression programming, Cu Mineralization