Bibliographic record
Abstract
The purpose of this paper is to characterise the source mechanisms of two large seismic events (magnitude Nuttli ≥ 3.0) using routine seismic data analysis tools. When there is no clear evidence of source mechanism type, there is a prevailing tendency to attribute fault slip mechanisms to large magnitude seismic events. This study uses seismic source parameter analysis to highlight characteristics of two large events that are more consistent with a stress-driven mechanism than a fault-related failure process. While fault-related seismicity tends to be confined to the plane of a geologic feature, stress-driven seismicity tends to be controlled by regions of mining-induced stress around mine voids and can migrate as mining progresses. Using seismic data from an ultra-deep open stoping mine in northern Quebec, this study characterises a migrating rock mass failure region in the mine abutments. The locations of seismic events, including mine-scale occurrences, are linked to the advancing stoping front of the mine abutment. Introducing a novel tool, plane-based time–distance charts, enables the exploration of migrating regions of rock mass yield and facilitates event clustering for source parameter analysis. The self-similarity of the large events with the broader migrating failure region is assessed using the Gutenburg–Richter frequency–magnitude relation. This analysis sheds light on the distinctive nature of stress-induced rock mass yield zones, providing insights for seismic hazard assessment in deep mining environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".