Delineation of Water Inflow in an Underground Potash Mine with 3‐D Electrical Resistivity Imaging
Bibliographic record
Abstract
Delineating water inflow in underground potash mining environments has routinely been done using conventional mining methods, seismic techniques and recently GPR as a useful non-invasive tool. The combination of highly resistive dry salt and highly conductive wet salt makes these water inflow areas a good candidate for Electrical Resistivity Imaging (ERI). Mosaic Potash, Golder Associates Ltd., and the University of British Columbia's Geophysical Inversion Facility (UBC-GIF) have worked to develop and apply ERI techniques for the underground environment. Because of the 3-D distribution of current and potential electrodes and the 3-D nature of the targets, full 3-D forward modeling and inversion of the data are required. The nature of underground mining limits the placement of electrodes to existing underground drifts and this severely restricts the available electrode geometry. By placing additional electrodes in boreholes, a survey geometry with enough information to constrain the 3-D inversion can be deployed. We present a case study of the delineation of a water inflow in a potash mine using 3-D ERI. The resulting inversion models of electrical conductivity have helped to focus drilling and mitigation efforts and have provided the geotechnical engineers and mine personnel with valuable information about the underground water distribution.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".