As mineral resources become increasingly scarce, the search for potential ore deposits presents a significant challenge in geological exploration. Subjective factors often constrain traditional knowledge-driven approaches, while purely data-driven methods may overlook the geological significance of data relationships, potentially compromising the accuracy and reliability of predictions. To address these issues, this study proposes a novel mineral prediction model that integrates fuzzy comprehensive evaluation with both knowledge-driven and data-driven approaches (FCEKDD), resulting in an optimized comprehensive mineral prediction model based on multi-source geoscience data. This model establishes comprehensive exploration indicators based on expert knowledge and quantitatively assesses these indicators through fuzzy evaluation methods to evaluate mineralization potential, thereby delineating exploration targets within the study area. Ultimately, a three-layer predictive framework is constructed using the C-A fractal method. Experimental results indicate that 57.1% of known mineral points are within the primary prediction zone, thus demonstrating the model’s high predictive accuracy. Comparisons with a random forest model reveal that the FCEKDD model has advantages in addressing geoscience data uncertainty and interpreting geological phenomena. This research validates the reliability and applicability of the proposed model in mineral exploration mapping, providing an effective solution for future mineral resource exploration.

The Jiaoxibei gold cluster is one of the most significant gold-producing regions in China and retains substantial regional prospecting potential. However, the superposition of multiple mineralization events has resulted in strong spatial coupling, multi-scale variability, and substantial redundancy among structural, alteration, geophysical, and geochemical information, limiting the effective extraction of key ore-controlling features. This study developed a geologically constrained feature-engineering framework for regional mineral potential evaluation. An initial indicator system comprising 32 geologically meaningful factors was constructed from structural geometry, remote-sensing alteration, gravity, magnetic, and geochemical information. The previously developed SOMML method was extended by introducing borehole-derived geological constraints to construct G-SOMML and generate the comprehensive geochemical anomaly factor Chem_F. Correlation-based redundancy reduction, Random Forest importance evaluation, SHAP interpretation, and geological screening were then combined to identify nine core factors. The selected factors were subsequently transformed according to their mineralization-response directions and integrated through category-balanced fuzzy synthetic evaluation. By linking geology-guided feature construction, borehole-constrained geochemical anomaly extraction, data-driven feature diagnosis, and balanced evidence integration, the framework provides a reproducible and interpretable workflow for organizing heterogeneous geoscientific information in regional mineral potential evaluation. The resulting high-potential zones captured 21 of the 22 known gold occurrences in the Sanshandao and Jiaojia areas and 17 of the 23 occurrences in the other parts of the study area, yielding an overall deposit capture rate of 84.4%. Compared with the all-feature fuzzy synthetic evaluation model (AT-Fuzzy), feature engineering reduced the high-potential area ratio from 50.951% to 25.356% while increasing the deposit capture rate from 51.1% to 84.4%. At the map level, the proposed framework delineated a substantially smaller high-potential area than the Random Forest model while achieving higher deposit capture rates than both the Random Forest and Weights of Evidence models under the common evaluation domain and statistical thresholding criterion. These results support the applicability of the framework for interpretable regional mineral potential evaluation and target prioritization in complex metallogenic districts.

To address the separated organization of structural and attribute information in three-dimensional geological modeling for coal mine production, this study presents a three-dimensional geological structure–property coupled modeling method implemented through an integrated voxel-based workflow. A regular voxel model is used as a unified spatial carrier to organize stratigraphic structures, coal seams, boundaries, reserve parameters, and gas content attributes within the same spatial framework. Structural information is first identified under surface and boundary constraints, and attribute values are then assigned within structurally constrained voxel domains. Using production data from the Hemei No. 3 Mine, the method was applied at both mine and working-face scales to evaluate its implementation, engineering consistency, sensitivity, and uncertainty. The results show that the proposed method can jointly represent the coal seam geometry and gas content distribution and can support spatial query and reserve updating. At the mine scale, the voxel-based reserve estimate for the No. 2-1 coal seam differed from the traditional geological block estimate by 1.67%. At the working-face scale, as an engineering consistency check, the relative difference between the voxel-based estimate and the engineering reference estimate decreased from 31% to 8.4% after additional borehole constraints were incorporated. Additional validation and sensitivity tests yielded quantified errors for gas content and working-face coal-seam geometry estimation. The results further show that reserve estimates are affected by voxel resolution and apparent density assumptions, whereas the spatial distribution of gas content estimates is affected by the tested IDW parameters. Therefore, the proposed method provides a practical application-oriented modeling and updating method under the data conditions of the case study, while broader transferability requires further independent validation and more systematic uncertainty analysis.