Variability and Trends in Selected Seismological Parameters During Longwall Mining of a Coal Seam Disrupted by a Rockburst
Bibliographic record
Abstract
Seismic tremors provide valuable insights into stress redistribution and accumulation, often serving as indicators of these processes within the rock mass, which can precede or accompany rockburst occurrences. Consequently, seismic monitoring is implemented in mines endangered by rockbursts to systematically assess the hazard conditions of mining openings. This study examines the variability and trends of selected seismological parameters, primarily the seismic energy of tremors observed during the longwall mining of the top layer of thick coal seam under challenging geological and mining conditions in an underground mine located in the Upper Silesian Coal Basin, Poland. The longwall mining operation was interrupted by a rockburst and subsequently discontinued. The analysis highlights both the cyclic variability and trends of seismological parameters, considering their dependence on extraction progress and temporal dynamics. The results indicate that mining progress is a significant factor influencing the stationarity of the seismic energy release process. It has been proposed that cumulative Benioff strain release is evaluated solely as a function of longwall face advancement. This illustrates the correlation between excavation progress and seismic energy accumulation. The trend analysis of this parameter, both over time and in relation to longwall face advancement, has also been conducted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".