Joint Inversion of Electromagnetic and Direct Current Resistivity Data Using Trust Regions. Application to Uranium Exploration in the Athabasca Basin
Bibliographic record
Abstract
ABSTRACT The Athabasca Basin (Saskatchewan, Canada) is a world‐class uranium mining province hosting high‐grade high‐tonnage deposits. Electromagnetic data and direct current resistivity data are essential tools to detect deep geoelectric structures associated with mineralization. Both methods are sensitive to electrical resistivity but highlight different structures. On the one side, electromagnetic methods reveal deeply buried, highly conductive graphitic structures. On the other side, direct current resistivity methods reveal milder contrasts of resistivity at shallower depths. We are here exploring the benefits that can be expected from two‐dimensional joint inversion of electromagnetic and direct current resistivity data for the exploration of unconformity‐related uranium deposits of the Athabasca Basin. Our methodology is recovering a single resistivity model to fit both datasets. We used a trust‐region globalization approach to regularize the local minimization sub‐problems, thus avoiding the task of regularization parameter tuning. Several tests are first conducted on synthetic models. These tests show that stand‐alone electromagnetic inversions are able to recover the position of conductive plates, but their geometry remains uncertain. On stand‐alone direct current resistivity inversions, the layered background is recovered, as well as smeared anomalies of resistivity associated with graphitic conductors. Whenever a conductive halo overlies a conductive plate, the wide anomaly associated with the plate appears more conductive and slightly shallower but the conductive plate and the halo cannot be distinguished. In the presence of two closely spaced conductors, the conductive anomaly appears more conductive and wider, so that they cannot be distinguished. On stand‐alone electromagnetic inversions, however, their separation is clear, but electromagnetic measurements are blind to alteration halos. Joint inversions give the most reliable models. Both the resistive background and the conductive plates are recovered. A better constrained background allows to recover more contrasted plates. Synthetic tests allowed us to confirm the potential to recover the footprint of a hectometric‐scale conductor overlying a plate using joint inversion, where both stand‐alone inversions failed. Following these synthetic tests, we present an application of our methodology to a dataset from the Waterbury–Cigar Lake area. Joint inversion allows to recover a geoelectric model reconciling both datasets. The model shows conductors better constrained below the depth of unconformity, allowing for interpretations of resistivity variations above them.
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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) | 0.000 | 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".