Investigating the Role of Foliation in Strain Bursts Using Seismic Moment Tensor Inversion and Advanced Numerical Modeling
Bibliographic record
Abstract
ABSTRACT: Strain burst incidents represent a significant challenge to safety and operational continuity within underground mining environments. These events not only endanger the lives of miners but also jeopardize infrastructure integrity, resulting in severe injuries, fatalities, and extensive financial losses. An understanding of the causes underlying strain bursts, particularly their relationship with foliation, is critical for effective prevention and hazard mitigation strategies, which have rarely been investigated. This study integrates Seismic Moment Tensor Inversion (SMTI) and advanced numerical modeling to examine the influence of foliation on strain bursts in underground mining. SMTI is used to analyze failure mechanisms associated with strain bursts, while numerical modeling, employing the Improved Unified Constitutive Model (IUCM), assesses energy release and volumetric strain as indicators of strain burst potential zones. The findings are validated through a case study in a deep Australian mine and additional examples from Canadian mines.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".