Three‐Dimensional Time‐Lapse Geoelectrical Monitoring of Water Infiltration in an Experimental Mine Waste Rock Pile
Bibliographic record
Abstract
Core Ideas 3D time‐lapse ERT is used to monitor water infiltration for mining environmental issues. Geoelectrical images provide information where no hydrogeological data is available. Water resistivity must be taken into account to understand bulk resistivity variations. Electrical resistivity of water is used as a tracer to reconstruct water infiltration. Infiltration model integrating both hydrogeological and geophysical data is proposed. Open‐pit mines often generate large quantities of waste rocks that are usually stored in waste rock piles (WRPs). When the waste rocks contain reactive minerals (mainly sulfides), water and air circulation can lead to the generation of contaminated drainage. An experimental WRP was built at the Lac Tio mine (Canada) to validate a new disposal method that aims to limit water infiltration into reactive waste rocks. More specifically, a flow control layer was placed on top of the pile, which represents a typical bench level, to divert water toward the outer edge. Hydrogeological sensors and geophysical electrodes were installed for monitoring moisture distribution in the pile during infiltration events. A three‐dimensional (3D) time‐lapse hydrogeophysical monitoring program was conducted to assess water infiltration and movement. Readings from the 192 circular electrodes buried in the WRP were used to reconstruct the 3D bulk electrical resistivity (ER) variations over time. A significant effort was devoted to assessing the spatiotemporal evolution of water ER because the bulk ER is strongly affected by water quality (and content). The water ER was used as a tracer to monitor the infiltration and flow of resistive and conductive waters. The results indicate that the inclined surface layer efficiently diverts a large part of the added water away from the core of the pile. Local and global models of water infiltration explaining both bulk and water ER variations are proposed. The results shown here are consistent with hydrogeological data and provide additional insights to characterize the behavior of the pile.
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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 | 0.000 |
| 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) | 0.003 | 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".