An Integrated Machine Learning Model for Optimizing Mineral Exploration Targets and Its Application

To address insufficient multi-source data fusion, strong subjectivity, low quantification, and difficulty in uncertainty quantification in traditional mineral exploration target optimization methods, this study constructs a probabilistic decision-making model integrating Hybrid Improved Fuzzy Analytic Hierarchy Process (HI-FAHP), Monte Carlo simulation, and machine learning. The model aims to achieve systematic fusion of multi-source information and probabilistic expression of results, improving the scientific reliability of exploration risk assessment in the early exploration stage. Taking the Zushimiao polymetallic mining area in Luanchuan County, Henan Province, as a case study, a HI-FAHP system is established by combining improved AHP, principal component analysis, and fuzzy evaluation. Weights are objectively determined for geological indicators (ore-bearing strata, fault structures, intrusive rocks, geophysical anomalies) and geochemical indicators (four groups: Ag-Pb-Sb, Au-Mo-As-Bi, Cu-V-Zn, W). Monte Carlo simulation is introduced to randomly perturb membership degrees (perturbation coefficient 0.08, triangular distribution) and grade boundary values (uniform distribution), generating probability distributions and ranking probabilities for comprehensive membership degrees of each target area. Three machine learning models—Decision Tree, Random Forest, and XGBoost—are trained on 5,000 randomly generated score datasets based on HI-FAHP weights. XGBoost is selected via error analysis to invert geological and geochemical weights and calculate the mineralization potential index. Five prospecting target areas (Ⅰ–Ⅴ) are delineated. Monte Carlo simulation yields comprehensive membership rankings: Target Ⅰ (79.55)>Target Ⅳ (76.70)>Target Ⅱ (74.50)>Target Ⅲ (71.30)>Target Ⅴ (67.20). XGBoost shows the best fit (R²=0.90), with inverted weights of 0.25 for geological and 0.75 for geochemical features, and consistent mineralization potential index ranking. Engineering verification confirms that Target I exposes galena ore bodies, Target IV delineates copper ore bodies, while the other targets show weak mineralization, highly consistent with predictions. The HI-FAHP-Monte Carlo-machine learning integrated model achieves objective multi-source data fusion and uncertainty quantification. XGBoost effectively inverts physically meaningful feature weights. Verified by engineering results, the model is reliable and suitable for multi-source data integration and exploration risk assessment in the early exploration stage..