Ashkan Seydi - School of Mining Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.
Maysam Abedi - School of Mining Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.
Abbas Bahroudi - School of Mining Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.
Hosein Ferdowsi - School of Mining Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.

The Birjand region is a part of the South Khorasan province, situated in the structural-magmatic zone of eastern Iran. As a part of the continental shelf, it forms from subduction during the Cenozoic and subsequent continental collisions. This region is favorable for copper and gold mineralization for various geological reasons. The ultimate goal of this study is to create a Cu geochemical potential map to delimit prone regions for further mining activities. A total of ۲۴۶۸ geochemical samples were gathered to run a ۲۰-element analysis. Taking data preprocessing approaches such as correction of outlier data and data normalization into consideration, a fractal graph through Concentration-Number (C-N) model was produced to isolate different geochemical populations of Cu, Pb, Zn, Ag, Ba, and Ni for Cu targeting. Then, a Prediction-Area (P-A) graph was plotted for each geochemical variable to determine the weight of each evidence map. The results show that Barium map indicates a prediction rate of ۷۲% and specifying ۲۸% of the studied areas as mineralization prone areas. The zinc geochemical map presents an ore prediction rate of ۶۵% and ۳۵% of area as potential zone. In addition, copper with an ore prediction of ۵۶% covered ۴۴% of the Birjand region. Finally, a hybrid evidence map was overlaid. Accordingly, the geochemical potential areas are further located towards the south and south-east of Birjand, which are closely related. Moreover, there are highly favorable areas in the middle part. It is noteworthy that the copper potential map has higher efficiency over each individual geochemical evidence, with an ore prediction rate of ۷۵% and occupying ۲۵% of the area as favorable zones.

Concentration-Number fractal (C-N), Prediction-Area fractal (P-A), MPM, Cu, birjand