Advanced geochemical exploration knowledge using machine learning: Prediction of unknown elemental concentrations and operational prioritization of Re-analysis campaigns
Bibliographic record
Abstract
In exploration geochemistry, advances in the detection limit, breadth of elements analyze-able, accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples to improve confidence in geochemical data and gain more insights into potentially mineralized areas. While a re-analysis campaign in a geochemical exploration program modernizes legacy geochemical data by providing more trustworthy and higher-dimensional geochemical data, especially where modern data is considerably different than legacy data, it is an expensive exercise. The risk associated with modernizing such legacy data lies within its uncertainty in return (e.g., the possibility of new discoveries, in primarily greenfield settings). Without any advanced knowledge of yet unanalyzed elements, the importance of re-analyses remains ambiguous. To address this uncertainty, we apply machine learning to multivariate geochemical data from different regions in Canada (i.e., the Churchill Province and the Trans-Hudson Orogen) in order to use legacy geochemical data to predict modern and higher dimensional multi-elemental concentrations ahead of planned re-analyses. Our study demonstrates that legacy and modern geochemical data can be repurposed to predict yet unanalyzed elements that will be realized from re-analyses and in a manner that significantly reduces the latency to downstream usage of modern geochemical data (e.g., prospectivity mapping). Findings from this study serve as a pillar of a framework for exploration geologists to predictively explore and prioritize potentially mineralized districts for further prospects in a timely manner before employing more invasive and expensive techniques.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".