## 4 Results and Discussion
### Model Comparison  
In this study, we conducted three experiments of prospectivity modeling using different sets of variables: (1) all variables, (2) only geometry variables, and (3) only geodynamic variables (simulation variables). To assess the impact of these variable sets on prospectivity modeling, we evaluated the regression and classification performance of three predictive models using various metrics. The regression model is conducted based on the gold amounts in each voxel. Table 3 presents performance comparisons of these regression models, encompassing error metrics (MAE, MSE, and RMSE) and correlation metrics (R and R2). The outcomes indicate that the all-variable model displays the least error (MAE of 2.74, MSE of 41.60, and RMSE of 6.45) in contrast to the geometry and simulation variable models. In terms of correlation, the regression results of the all-variable model exhibit the highest significance (R of 0.92 and R\({}^{2}\) of 0.85) when compared to actual gold amounts. This is followed by the geometry variable model (R of 0.79 and R\({}^{2}\) of 0.62), whereas the simulation variable model shows the lowest significance (R of 0.74 and R\({}^{2}\) of 0.55). In the assessment of classification performance, we used the receiver operating characteristic (ROC) curve [102], the area under the ROC curve (AUC), accuracy, precision, F1_score and kappa. The shape of the ROC curves reflects the predictive performance, and a curve that is convex towards the upper-left corner indicates better model performance. The AUC is a quantitative parameter of a ROC curve, similar to the other four metrics, where values closer to 1 indicate better model performance. Figure 11 illustrates the ROC curves and calculation of AUC, accuracy, precision, F1_score and kappa. Our findings reveal that the performance of the model incorporating all variable sets (red line) demonstrates significantly better performance than the two models based on individual variable sets, with the largest AUC (0.92), accuracy (0.85), precision (0.87), F1_source (0.87) and kappa (0.7; Figure 11). Besides, the geometry variable model (marked by the blue line) presents superior predictive capability than the simulation variable model (marked by the green line) but is not remarkable (AUC: 0.82 vs. 0.74; accuracy: 0.77 vs. 0.70; precision: 0.73 vs. 0.70; F1_source: 0.82 vs. 0.76; kappa: 0.50 vs. 0.38). Furthermore, we used some reserved areas that are detected by drill holes to verify the generalization ability of three prospectivity modes (Figure 12). The results reveal that prospectivity models utilizing only single geometric or geodynamic variables yield the wrong prediction at depth (Figure 12; green arrow), while the model using all variables performs satisfactorily. These findings suggest that: (1) the geometry variables are more advantageous in indicating mineralization distribution compared to simulation variables; \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **MAE** & **MSE** & **RMSE** & **R** & **R\({}^{2}\)** \\ \hline all-variable model & 2.74 & 41.60 & 6.45 & 0.92 & 0.85 \\ geometry variable model & 3.26 & 44.89 & 6.70 & 0.79 & 0.62 \\ simulation variable model & 4.44 & 65.44 & 8.09 & 0.74 & 0.55 \\ \hline \hline \end{tabular} Note: MAE, mean absolute error; MSE, mean squared error; RMSE, root mean squared error; R, correlation coefficient; R\({}^{2}\), coefficient of determination.  
\end{table} 
Table 3: Performance comparisons of three regression models. Figure 11: Performance comparisons of three classification models. **(a)** ROC curves and **(b)** radar chart of accuracy–precision–AUC–kappa-F1 score. (2) the combination of the two variable sets dramatically enhances the model's performance; in other words, the incorporation of simulation-related variables allows the prospectivity model to more comprehensively represent the quantitative associations between gold mineralization and its controls factors.