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Evidential 3D swin transformer–CNN for uncertainty-aware blind gold prospectivity mapping: A case study from Siahcheshmeh, NW Iran | ||
| International Journal of Mining and Geo-Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 23 اردیبهشت 1405 | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijmge.2026.406125.595334 | ||
| نویسندگان | ||
| Salman Farahani؛ Abbas Bahroudi* | ||
| School of mining engineering, university of Tehran, Tehran, Iran. | ||
| چکیده | ||
| Discovering blind gold deposits beneath post-mineral cover remains a major challenge in mineral exploration, where drilling decisions are high-risk and capital intensive. Conventional 2D prospectivity mapping and deterministic 3D machine-learning models fail to capture subsurface geological complexity and do not provide quantitative measures of predictive confidence. This study introduces an Evidential 3D Swin Transformer–CNN framework for uncertainty-aware blind gold prospectivity mapping. The hybrid architecture integrates multi-scale 3D convolutional feature extraction with hierarchical Swin Transformer self-attention to model local geological signatures and kilometre-scale structural controls. An evidential learning head under a Dirichlet prior enables analytical decomposition of aleatoric and epistemic uncertainties in a single forward pass, eliminating Monte Carlo overhead. The framework was applied to the Discovering blind gold deposits beneath post-mineral cover remains a major challenge in mineral exploration, where drilling decisions are high-risk and capital intensive. Conventional 2D prospectivity mapping and deterministic 3D machine-learning models fail to capture subsurface geological complexity and do not provide quantitative measures of predictive confidence. This study introduces an Evidential 3D Swin Transformer–CNN framework for uncertainty-aware blind gold prospectivity mapping. The hybrid architecture integrates multi-scale 3D convolutional feature extraction with hierarchical Swin Transformer self-attention to model local geological signatures and kilometre-scale structural controls. An evidential learning head under a Dirichlet prior enables analytical decomposition of aleatoric and epistemic uncertainties in a single forward pass, eliminating Monte Carlo overhead. The framework was applied to the Siahcheshmeh intrusion-related gold system (NW Iran) using twelve co-registered 3D evidence layers at 10 m voxel resolution. On an independent test set of 97,650 voxels from 64 previously undrilled boreholes, the model achieved ROC-AUC of 0.981 and PR-AUC of 0.967, while maintaining low epistemic uncertainty across the domain. High-confidence blind targets, defined by combined probability and evidence strength thresholds, coincide with fault intersections and phyllic-altered intrusive rocks, consistent with geological controls and drilling outcomes reported in the study area. These results demonstrate that integrating global structural modeling with uncertainty quantification enables risk-informed 3D prospectivity mapping in covered terranes. | ||
| کلیدواژهها | ||
| 3D mineral prospectivity mapping؛ Uncertainty quantification؛ Swin transformer؛ Evidential deep learning؛ Gold mineralization | ||
| مراجع | ||
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