A generative neural network approach to uncertainty and risk-return analysis in mineral prospectivity modelling
Bibliographic record
Abstract
The industrial adoption of machine learning techniques for mineral prospectivity modelling (MPM) remains limited due to their inability to model uncertainties and a lack of systematic frameworks for evaluating risk and return in mineral predictions. A major challenge is that most existing methods fail to simultaneously capture both epistemic uncertainty, which arises from limitations in the predictive modelling process, and aleatoric uncertainty, which stems from the inherent randomness in geoscience data. To address this, we propose a conditional variational autoencoder (CVAE) approach incorporating decoder calibration and uncertainty estimation, which we apply to Canadian magmatic Ni (±Cu ±Co ±PGE) sulphide mineral systems. Aleatoric uncertainty is quantified from the CVAE’s posterior distribution, whereas epistemic uncertainty is assessed from 100 MPM realizations based on datasets generated by the CVAE. We also introduce a novel risk-return framework which integrates relative uncertainty measures with the non-parametric Getis–Ord G ∗ statistics spatial clustering technique to categorize exploration targets into four distinct risk-return categories. Results from the spatial distribution and kernel density estimation analysis reveal that most known deposits are situated in low-uncertainty zones. Notably, high-return zones, which comprise approximately 4% of the total area, account for 94.7% of the known deposits. This research highlights the significance of incorporating uncertainty and risk-return analysis to improve decision-making in mineral prospecting.
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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 | 0.003 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".