Spatio-temporal analysis and volumetric characterization of interferometric synthetic aperture radar-observed deformation signatures related to underground and in situ leach mining
Bibliographic record
Abstract
The effect of uranium mining on ground deformation is a relatively unexplored area, especially in terms of surface subsidence related to subsurface ore removal. We use interferometric synthetic aperture radar and spatiotemporal techniques to characterize subsidence signals at the McArthur River underground mine in Canada and the Four Mile in situ leach mine in Australia. We enhance the signal-to-noise ratio of our datasets via time-series techniques and compare results from active periods with results during inactivity to establish a baseline for mining-related signals. We then relate observed surface subsidence to subsurface volumetric strain rates via a voxel parameterization and Bayesian, geostatistical inversion. We use priors on our volumetric strain rates to identify whether these rates are best attributed to ore removal or if additional factors are contributing to subsidence at these sites. We find that the subsidence at McArthur River is best explained by a combination of ore removal and thermal contraction resulting from ground freezing practices. Ore removal via solution extraction alone explains the subsidence at Four Mile, although the localized subsidence pattern and resulting strain rates suggest an intricate combination of sinks and sources in the field, possibly from injection and production well locations and the subsequent flow of solution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".