Hydraulic Tomography for Characterizing Subsurface Heterogeneity in Fractured Rocks: Implications for In-Situ Recovery Mining
Bibliographic record
Abstract
In-situ recovery (ISR) mining demands reliable forecasts of groundwater flow and solute transport, which are highly sensitive to subsurface heterogeneity. This study evaluated alternative modeling approaches for characterizing heterogeneity using data from Denison’s Wheeler River Property, Saskatchewan, Canada. Spatial distributions of hydraulic conductivity (K) and specific storage (Ss) were characterized using four modeling cases: a homogeneous case, a geology-based zonation case, and two hydraulic tomography (HT) cases. All models were calibrated against 17 cross-hole aquifer tests, with one HT model additionally constrained by long-term feasibility field test (FFT) responses. Estimated K and Ss fields from four cases were subsequently validated against independent tracer test data. Results revealed systematic improvements in predictive performance with increasing model complexity, with HT models reproduced observed hydraulic responses and breakthrough curves with higher fidelity. Incorporating FFT data further enhanced delineation of large-scale hydraulic connectivity and improved BTC prediction at the more distant extraction well. These findings underscore that treating K and Ss fields to be heterogeneous and accurately characterizing their spatial distributions are essential for realistic simulations of groundwater flow and solute transport in ISR operations. Overall, we conclude that HT is an effective approach to support ISR wellfield design, recovery optimization, and environmental protection.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".