Document Type : Original Research Paper

Authors

• Mobin Saremi ¹
• Saeed Yousefi ²
• Mahyar Yousefi ³

¹ Department of Mining Engineering, Faculty of Engineering, Malayer University, Malayer, Iran

² Department of Mining, Faculty of Engineering, University of Birjand, Birjand, Iran

³ Faculty of Engineering, Malayer University, Malayer, Iran

Abstract

The Mineral Prospectivity Mapping (MPM) is a procedure of integrating various exploration data to identify promising areas for follow up mineral exploration programs. MPM facilitates identification of mineral deposit prospects through reducing search spaces for the purpose of mitigating cost and time shortages. In this regard, geochemical anomaly maps constitute one of the most important evidential layers for MPM. In this research work, to produce an efficient geochemical evidential layer, the Staged Factor Analysis (SFA) method and Geochemical Mineralization Probability Index (GMPI) were performed on a dataset of 657 stream sediment samples. In addition to the mentioned maps, a layer of proximity to faults was used to efficiently identify the intended targets of copper hydrothermal deposits. The layers were then weighted and combined using logistic functions and the geometric average method. Based on the obtained results, the promising areas were found in three parts including western, central, and northern areas, which correspond to the faulted units of andesite, tuff, granite, and granodiorite intrusive masses. Finally, in order to evaluate the generated model, the prediction-area (P-A) plot was used, which shows the relative success of the generated map in specifying the desired exploration targets. The P-A plot showed that this model has a prediction rate of 64%. It seems that the proposed method by considering multi-element geochemical signatures and combination by another exploratory layer target the promising areas, those that are simultaneously present with other exploration evidence.

Keywords

• Stage Factor Analysis
• Geochemical Mineralization Probability Index
• Geometric average
• Mineral Prospectivity Mapping
• Feizabad

Main Subjects

• GIS & Remote Sensing