Dynamic triggering of microseismicity in an underground mine
Bibliographic record
Abstract
Summary We examine spatio-temporal patterns of microseismicity recorded during one month in an underground mine by addressing three key questions: 1) where does the seismicity occur? 2) Why does it occur in these locations? And 3) what triggers it? To obtain accurate locations, we perform a multiplet analysis and use a modified version of the double-difference (DD) relocation method. This approach leads to highly accurate relative event locations and requires groups of multiplets only. Most of the 281 relocated events are close to the main shaft and tunnels; thus we postulate seismicity is facilitated by stresses associated with the potential for subsidence in addition to the hoop stresses acting on the two vertical shafts. Most events occurred during certain hours of the day and there is a 68% correlation with reported rock removal; therefore, it is likely they were triggered by static and dynamic stress perturbations caused by the transportation of debris along tunnels instead of our initial guess that blasting was the principal causative mechanism. Given that seismicity is present around the main shaft but absent close to the second one, we conclude that for seismicity to occur both a favorable stress state and additional external perturbing forces must exist, thus leading to dynamic event triggering in an initially stable stress situation. This analysis provides more insight into anthropogenic processes that might trigger seismicity, thereby facilitating identification of hazardous and potential damage areas in mine settings.
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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 | 0.000 | 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".