Mineral prospectivity mapping using a VNet convolutional neural network
Bibliographic record
Abstract
Abstract Major mineral discoveries have declined in recent decades, and the natural resource industry is in the process of adapting and incorporating novel technologies such as machine learning and artificial intelligence to help guide the next generation of exploration. One such development is an artificial intelligence architecture called VNet that uses deep learning and convolutional neural networks. This method is designed specifically for use with geoscience data and is suitable for a multitude of exploration applications. One such application is mineral prospectivity in which the machine is tasked with identifying the complex pattern between many layers of geoscience data and a particular commodity of interest, such as gold. The VNet algorithm is designed to recognize patterns at different spatial scales, which lends itself well to the mineral prospectivity problem of there often being local and regional trends that affect where mineralization occurs. We test this approach on an orogenic gold greenstone belt setting in the Canadian Arctic where the algorithm uses gold values from sparse drill holes for training purposes to predict gold mineralization elsewhere in the region. The prospectivity results highlight new target areas, and one such target was followed up with a direct-current induced polarization survey. A chargeability anomaly was discovered wherein the VNet had predicted gold mineralization, and subsequent drilling encountered a 6 g/t Au intercept within 10 m of drilling that averaged more than 1.0 g/t Au. Although most of the prospectivity targets generated from VNet were not drill tested, this first intercept helps validate the approach. We believe this method can help maximize the use of existing geoscience data for successful and efficient exploration programs in the future.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".