Accurate prediction of mineral concentrations from sparse exploration data is important for resource estimation. This study evaluates hybrid prediction models combining machine learning (ML) and geostatistics to predict aluminum (Al) concentrations. Twelve hybrid configurations were generated by combining six ML backbones—Random Forest, XGBoost, AdaBoost, ResNet, U-Net, and Spatial Transformer Network—with Ordinary Kriging (OK) and Universal Kriging (UK). Model performance was evaluated using 10-fold spatial cross-validation (CV) to reduce spatial leakage, and hyperparameters were tuned by grid-search CV within the training folds. For the hybrid models, residual kriging was fitted using cross-fitted out-of-fold residuals to reduce optimistic bias and prevent information leakage. The results showed no consistent performance separation between OK and UK variants. More importantly, the effect of integration was backbone dependent rather than uniformly beneficial. RF-based predictions showed the strongest overall out-of-sample performance, whereas hybrid gains for other backbones were generally modest. After multiple-comparison correction, most differences between standalone and hybrid models were not statistically significant. These findings indicate that increasing model complexity through hybridization does not guarantee improved accuracy and highlight the importance of spatially explicit, bias-aware evaluation when selecting prediction strategies for mineral resource exploration.
Han et al. (Wed,) studied this question.