Fardin Ahmadi - Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
Hamid Aghajani - Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
Maysam Abedi - School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

This study serves the purpose of generating a geochemical Fe-bearing potential map. Stream sediment geochemical survey was employed by collecting ۸۴۳ samples for analyzing ۱۹ elements and oxides. Taking preprocessing of data (e.g. outlier correction and data normalization) into consideration, a Concentration–Number (C-N) fractal model was used to separate different geochemical populations of Fe۲O۳, TiO۲, V and the main multi-element factor in close spatially association with the Fe targeting. A prediction-area (P-A) plot was drawn for each variable to determine the weight of each geochemical indicator. Results indicate that the main geochemical factor with an ore prediction rate of ۷۳%, has occupied ۲۷% of the Esfordi area as favorable zones for further mining propsectivity. The Esfordi as a favorable Fe-bearing zone is of special interest in the NE of the Bafq mining district that hosts important “Kiruna-type” Magnetite-Apatite deposits. In addition, a synthesized indicators map was prepared through implementing a data-driven multi-class index overlay in a similar fashion to the previous version of the method, upon which geochemical potential zones were mostly in the NE part of the Esfordi, intimately linked with intense fault density map. The significance of this study lies in localizing of the most geochemical favorable zones through simultaneous consideration of the C-N and P-A plots accompanied with the incorporation of known active mines and prospects to determine indicator weight. Of note is that the Mineral Potential Mapping (MPM) has higher efficiency over each geochemical indicator with an ore prediction rate of ۷۸% and area occupation of ۲۲%.

Esfordi, Stream Sediment, C-N fractal model, P-A plot, Data-driven index overlay