Geophysical inversion contributions to mineral exploration: lessons from the Footprints project
Bibliographic record
Abstract
Magnetic and gravity inversions are used to create 2D or 3D models of the magnetic susceptibility and density, respectively, using potential field data. Unconstrained inversions generate an output based on mathematical constraints imposed by the inversion algorithm. Constrained inversions integrate lithological, structural, and petrophysical information in the inversion process to produce more geologically meaningful results. This study analyses the validity of this assertion in the context of the NSERC-CMIC Mineral Exploration Footprints project. Unconstrained and constrained geophysical inversions were computed for three mining sites: a gold site (Canadian Malartic, Québec), a copper site (Highland Valley, British Columbia), and a uranium site (Millennium – McArthur River, Saskatchewan). After initially computing unconstrained inversions, constrained inversions were developed using physical property measurements, which directly link geophysics to geology, and lithological boundaries extracted from an interpreted geological model. While each derived geological model is consistent with the geophysical data, each site exhibited some magnetic complexity that confounded the inversion. The gold site includes regions with a strong magnetic signature that masks the more weakly magnetic zone, thereby hiding the magnetic signature associated with the ore body. Initial unconstrained inversions for the copper site yielded solutions with invalid depth extent. A consistency between the constrained model and the geological model is reached with iterative changes to the depth extent of the model. At the uranium site, the observed magnetic signal is weak, but the inversion provided some insights that could be interpreted in terms of an already known complexly folded geological model.
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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 | 0.001 |
| 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) | 0.001 | 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 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".