Clustering and constrained inversion of seismic refraction and gravity data for overburden stripping: Application to uranium exploration in the Athabasca Basin, Canada
Bibliographic record
Abstract
ABSTRACT Gravity signatures from features associated with the footprints of uranium deposits within the sandstones and basement of the Athabasca Basin are masked in the measured gravity by the contribution from glacial sediments (overburden), in particular by the variable thickness of the overburden. The 2D inversions of seismic refraction and gravity data are assessed as a means of reliably mapping overburden thickness, enabling the contribution to gravity from the overburden to be taken into account and density anomalies associated with deeper mineralization and alteration to be reconstructed through further inversion. Results show that independent inversion of seismic refraction data using the fuzzy c-means clustering method is able to determine the base of overburden well. Subsequent gravity inversion constrained by the overburden thickness reveals possible subtle density variations at depth, which could be associated with alteration in the sandstones associated with the uranium mineralization. Application of the seismic clustering inversion followed by constrained gravity inversion to both representative synthetic scenarios and real data from the Athabasca Basin, Canada, are considered. Drill-hole data show that the inversion results can predict the base of the overburden well, and there is an acceptable match between geologic information and possible alteration zones suggested by the inversions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".