This study focuses on the integration of geochemical data analysis methods to enhance mineral exploration in the Siah Cheshmeh region of the Khoy Ophiolite in northwestern Iran. Initially, all preprocessing steps were applied to the geochemical data to ensure its quality and accuracy before proceeding with the main analysis. Staged Factor Analysis (SFA) was then employed to identify key elements closely related to mineralization processes, such as Au, Cu, Fe, Mn, Zn, Cr, and Ni, due to their significant roles in mineral enrichment and deposit formation. These elements were subsequently analyzed using Exploratory Data Analysis (EDA) and fractal modeling techniques to differentiate background from anomaly concentrations, effectively detecting high-anomaly areas indicative of mineral-rich zones. For example, the anomaly area for manganese using the fractal method was determined to be 4,551,879 square meters, compared to 22,107,400 square meters obtained using EDA, highlighting the fractal method's higher precision in mapping anomalies. The SFA approach was further refined using the Geochemical Mineralization Potential Index (GMPI), with calculations performed for the associations GMPIAu-Cu-Fe-Mn-Zn and GMPICr-Ni. GMPI values were determined based on the 95% cumulative frequency, with additional thresholds at 99% and 97.5% to highlight higher-intensity anomalies. This comprehensive integration of SFA, EDA, fractal modeling, and GMPI methods produced detailed geochemical maps that precisely identify prospective exploration areas, emphasizing regions with high mineral potential. The study demonstrates the effectiveness of combining these techniques to guide focused geochemical investigations for mineral exploration.

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.