Geologically constrained data-driven modeling for mineral prospectivity mapping
Bibliographic record
Abstract
Abstract The discovery and development of mineral resources are critical for sustaining modern energy demands. However, the geological processes that control mineralization are inherently complex, introducing considerable spatial variability that presents significant challenges for predictive modeling. While machine learning approaches have been increasingly applied to mineral prospectivity, many fail to explicitly incorporate key geological constraints, limiting their capacity to resolve the nonlinear and directionally dependent nature of mineralizing systems. Here we present a geologically constrained data-driven method that explicitly accounts for the spatial non-stationarity and anisotropy in ore-forming processes. In the benchmark case study from Canada, our method demonstrates a 7.4% improvement in recall performance compared with existing models. This robust performance is also observed in applications to the southern Cordillera region. Furthermore, the method elucidates regional ore-forming controls and quantifies spatial anisotropy in porphyry copper systems. Our findings demonstrate that incorporating geological constraints into data-driven models enhances both the accuracy and interpretability of mineral prospectivity assessments, offering a robust path forward in resource exploration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".