Analysis of induced seismicity at Young-Davidson mine
Bibliographic record
Abstract
As the demand for mineral resources is on the rise and mining operations continue to dig deeper at higher mining rates, the risks associated with mining-induced seismicity have substantially increased. Strong seismic events can cause rock mass and support system damage in drifts and stopes, resulting in production delays; more importantly, they may pose a hazard to the safety of mine operators. Thus the causes and risks associated with mining-induced seismicity must be investigated. This paper reports on the results of a case study at Young-Davidson (YD) mine in Canada. The YD mine is experiencing large seismic events at different mining horizons. The focus of this study is the MW2.0 events occurring in the lower mine in the depth range of 900 to 1,200 m below surface. The goal is to identify the root causes behind the large seismic events and suggest remedial strategies. The analysis of seismic source parameters and moment tensor inversion of five large seismic events helped identify the source mechanisms. In situ stress measurements previously conducted at the YD mine were analysed and used in a mine-wide numerical model that was generated with FLAC3D, taking into consideration the northeast-trending diabase dykes. The model simulates mining-induced stress distribution following the mine plan of primary and secondary stope extraction. Qualitative assessment of the safety factor, brittle shear ratio and stored strain energy, as well as comparison with seismic source location, magnitude and mechanism, helped provide an understanding of the seismic behaviour in the lower mine. The study revealed that strong seismic activities are attributed mainly to high pre-mining differential stress ( with running parallel to the dykes. This leads to high differential stress build-up in the secondary stopes (ore pillars) and sill pillars, which causes predominantly compressive/shear seismic source mechanisms. The research completed by Khalil (2023) forms the basis of this paper.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".