Event tree analysis for room and pillar mining affecting permeability beneath surface bodies of water
Bibliographic record
Abstract
Underground mining and oil and gas drilling have increasingly encroached on public water reservoirs and dams because of the overwhelming demand for energy combined with the growing population. Cases of surface water reservoirs and mine waste impoundments being drained, as well as dam infrastructure being damaged, due to accidents have been documented. The methods used by regulators and industry for determining mining or drilling offset distances are based primarily on three approaches and studies performed in the early 1970s. The former US Bureau of Mines Information Curricular 8741 was the culmination of these studies and continues in use for determining offset distances for underground mining with respect to dams and reservoirs. This study used analytical and empirical methods based on subsidence effects to recommend offset distance extents specifically for miners' safety. A major limitation of the previous studies is that they did not detail the affect mining operations have on changes in groundwater flow due to soil and overburden permeability changes triggered by vertical ground surface subsidence that could lead to increased risk hazards for a reservoir or dam infrastructure. This paper presents a review of international literature related to mining under surface bodies of water (reservoirs) and presents a risk-based event tree analysis quantifying the probability of changes in subsurface permeability due to overburden strain changes. A sensitivity analysis quantifying probabilities of increased subsurface permeability in terms of offset distances from a reservoir is presented and discussed. Empirical results indicate the probability of permeability changes at a 350 ft (107 m) deep mine located at offsets of: 200, 400, and 600 ft (61, 122, and 183 m) were at 41, 0.66, and 0.0067%, respectively.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 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".