Application of Machine Learning to Characterizing Magma Fertility in Porphyry Cu Deposits
Bibliographic record
Abstract
Abstract Large and easily accessible porphyry Cu deposits have already been identified, exploited, and gradually exhausted. Novel strategies, therefore, are required to identify new, deeply buried deposits. Previous studies have proposed several lithogeochemical and mineralogical approaches for identifying porphyry Cu systems. Most of these methods, however, require significant a priori knowledge of the exploration region and are, generally, of low effectiveness. In this study, machine learning models using Random Forest and Deep Neural Network algorithms are utilized to characterize magma fertility. The two models have first been trained using a large trace‐element data set of magmatic zircon and then validated on unseen data set during the training process. The performance of both models was evaluated using a fivefold cross‐validation technique, which demonstrates that the models provide consistent results and yield good classification accuracy (∼90% on average) with low false positive rates. Feature importance analysis of the models suggests that Eu/Eu*, Eu/Eu*/Y, Ce/Nd, Ce/Ce*, Dy, Hf, and Ti are the important parameters that distinguish fertile and barren zircons. The real‐world applicability of the validated models was evaluated using two well‐characterized porphyry Cu deposits in subduction and postcollisional settings—the Highland Valley porphyry Cu district (south‐central British Columbia, Canada) and the southern Gangdese belt (Tibet, China), respectively. The results demonstrate that our generalized models can discriminate zircon from igneous rocks associated with porphyry Cu deposits from those in nonmineralized systems with high accuracy and independent of geological setting, suggesting that this new approach can be used effectively in greenfield and brownfield 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".