Machine Learning-Based Classification of Rock Bursts in an Active Coal Mine Dominated by Non-Destructive Tremors
Bibliographic record
Abstract
Rock bursts are dynamic phenomena in underground openings, causing damage to support and infrastructure, and are one of the main natural hazards in underground coal mines. The prediction of rock bursts is important for improving safety in mine openings. The hazard of rock bursts is correlated with seismic activity, but rock bursts are rare compared to non-destructive tremors. The five machine learning classifiers (multilayer perceptron, adaptive boosting, gradient boosting, K-nearest neighbors, and Gaussian naïve Bayes), along with an ensemble hard-voting classifier composed of these classifiers, were used to recognize rock bursts among the dominant non-destructive tremors. Machine learning models were trained and tested on ten sets of randomly selected data obtained from one of the active hard coal mines in the Upper Silesian Coal Basin, Poland. For each of the 627 cases in the database, 15 features representing geological, geomechanical, mining, and technical conditions in the opening as well as tremor energy and correlated peak particle velocity were determined. Geological and geomechanical parameters of the coal seams and surrounding rocks were aggregated into a single GEO index. The share of rock bursts in the database was only about 8.5%; therefore, the ADASYN balancing method, which addresses imbalanced datasets, was used. The ensemble hard-voting classifier most effectively classified rock bursts, with an average recall of 0.74.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".