Pyrite geochemistry for deposit type prediction and exploration in the Golden Triangle, northwest British Columbia, Canada
Bibliographic record
Abstract
Mineral exploration in remote mountain belts represents an exceptional challenge due to high operational costs, limited accessibility, and complex geology. New tools are urgently needed to improve discovery rates in these types of highly prospective but challenging mineral exploration frontiers. Herein we apply deep learning to predict deposit types in a rugged and remote part of northwest British Columbia (Canada) based on laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) analysis of pyrite to address that knowledge gap. The combined pyrite dataset represents four mineral deposit types at various stages of development, including porphyry copper-gold (i.e., Galore Creek, Copper Canyon, Kerr, Mitchell, Sulphurets, Iron Cap), epithermal gold-silver (i.e., Brucejack), magmatic nickel-copper (i.e., E&L), and volcanogenic massive sulphide copper-lead-zinc (i.e., A6). Trace element mapping, spot analysis, and quantitative mineralogy are applied to characterize the petrogenetic context and composition of each pyrite sample. Geochemical data were first pre-processed with principal component analysis and autoencoders to extract new features from the training data. The pre-processed pyrite data were then combined to train a series of feed-forward artificial neural networks to predict deposit types. The preferred deep learning classification model yields an overall accuracy of 99% for a subset of pyrite analyses that were not included in the training process. We then apply that classifier to show that hydrothermally altered rocks from early-stage mineral exploration projects (i.e., Dok and Yeti) are most likely related to porphyry copper-gold mineralization based on the composition of pyrite. Statistical analysis of the model results further demonstrates that pyrite morphology, texture, grain size, and paragenesis are important predictors of deposit type when combined with trace element concentrations and the new latent variables identified by the preferred autoencoder. We suggest that the pyrite library and modelling methodology can be used to support early-stage mineral targeting in remote exploration frontiers that are prospective for a range of deposit types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".