## 4 Results and Discussion
### Three-Dimensional Metalogenic Prediction
#### 4.3.3 Three-Dimensional Prospectivity Mapping Based on BPUL  
In this study, 183,390 voxels from positive samples and 2,540,043 voxels from unlabeled samples were input into the PU learning model. The Bagging-based PUL algorithm optimizes the hyperparameters of its base learner (Random Forest) through Bayesian hyperparameter optimization in each iteration. The hyperparameter search space and optimal hyperparameter selection for Bayesian optimization are shown in Table 7. After 10 iterations, the BPUL model achieved a prediction accuracy of 0.98, a recall rate of 0.98, and an \(F1\) score of 0.99, demonstrating high predictive performance. The optimal model had n_estimators set to 35, max_depth set to 481, and min_samples_split set to 12 (Table 8). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Parameters** & **Skopt Best Parameter** & **Search Range** \\ \hline RF & n\_estimators & 35 & [1, 100] \\  & max\_depth & 481 & [1, 500] \\  & min\_samples\_split & 12 & [1, 20] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Hyperparameter adjustments of BPUL. Figure 20: Three-dimensional deposit-scale model of the (**a**) proximity of carbonatization, (**b**) chloritization, (**c**) migmatization, (**d**) fault, (**e**) magnetic anomaly. The performance of the BPUL model was evaluated using both ROC analysis and the P-V plot. The AUC value of 0.98 indicates that the BPUL model performs well in predicting the potential areas of the Yanshan ore deposit (Figure 21a). The intersection point in the P-V plot shows that, based on the BPUL model, 90% of the known ore voxels are delineated within 10% of the study area (Figure 21b). This identifies the high-prospect region as a potential target area for future mineral exploration. By selecting the optimal threshold from the P-V plot intersection, the high-prospect area is separated from the low-prospect background area. As shown in Figure 22a, the high-prospect area (with a probability > 0.75) has a strong correlation with the known ore bodies and exhibits a consistent spatial distribution with the fault zones, aligning with the geological features of the region. Figure 22b is the result map of the mineralization probability, further determined based on the high mineralization region shown in Figure 22a. According to the high mineralization belt depicted in Figure 22a, two key target areas were identified, located in the northwest and deep extension zones of the mining area, which are similar to the current exploration potential location of the Yanshan Iron Mine [46]. In the northwest of the mining area, the high mineralization belt predicted by the BPU model is significantly larger than the known ore body and extends outward. Figure 22b indicates that there is still a high mineralization belt along the 45\({}^{\circ}\) dip direction of the ore body in the deep part of the mining area. However, based on the current data, the mineralization potential deeper than 1000 m remains unclear. The next step will involve drilling tests within the defined areas. Combining the elemental comparison and hyperspectral rock mineral testing results from this study, the characteristics of alteration, magnetism, etc., from the new drill holes will be analyzed to further improve the three-dimensional mineralization prediction results. The SHAP algorithm was used for feature importance analysis on the optimal trained model. The results show that chloritization, mignatization, and magnetic anomalies play significant roles in iron mineralization prediction (Figure 23). As shown in Figure 24a, hydrothermal alteration near the iron-rich ore bodies is evident, mainly dominated by chloritization, which is closely related to mineralization. Figure 24b indicates that the widespread hybridization in this area may have provided hydrothermal fluids for the alteration of low-grade ore and is hosted in the upper part of the ore body. Although the Yanshan BIF iron ore deposit is controlled by folding and faulting, the contribution of the NE-trending faults is relatively small.