Forward modeling and 3D inversion of electromagnetic data collected over the McArthur River uranium deposit in the Athabasca Basin, Canada
Bibliographic record
Abstract
ABSTRACT Detection and assessment of the deeply buried high-grade uranium deposits in the Athabasca Basin rely on geophysical methods to map conductive rocks. Variable Quaternary surface cover can mask the anomalous signals from depth and affect the interpretation of inverted conductivity models. We present the analysis of several electromagnetic (EM) modeling studies and two field data sets to demonstrate the effects of varying Quaternary cover resistivity and thickness, on the ability to resolve the parameters of underlying sandstone, alteration, and basement conductors. Synthetic data, assuming a typical shallow EM sounding system and realistic resistivities found in the Athabasca Basin, indicate that resistivity and thickness parameters of the Quaternary cover can be separately recovered in cases in which this cover is more conductive than the underlying sandstone, but not when the cover is significantly more resistive. A 3D modeling study indicates that by using airborne EM data, it is possible to detect a basement conductor of 20 S at a depth of at least 600 m below the surface, even in the presence of Quaternary cover thickness variations of the up to 20% (40–60 m). Furthermore, although Quaternary cover variations and deeper sandstone alteration can produce comparable anomalous signal amplitudes in a time-domain EM response, their effects are most visible in distinctly separate time windows. Ground-penetrating radar and other data to characterize the Quaternary cover in the McArthur River area indicate that this cover consists mostly of sandy tills ranging in thickness from 0 to 117 m. Constrained 3D inversion of an airborne EM data set from the same area indicates basement conductors consistent with the depth and location of a known fault. Elevated conductivity in the sandstone by up to a factor of two over the background values could indicate possible alteration.
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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 | 0.000 | 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".