Microseismic monitoring of rockbursts with the ensemble Kalman filter
Bibliographic record
Abstract
ABSTRACT We introduce an algorithm to monitor seismic velocity changes associated with rockbursts in mines, through microseismic monitoring. Rockbursts are extreme events resulting from the complex interaction between mining activities and geology, and represent a significant threat to mines. In recent years, the use of passive seismic monitoring for mine safety and productivity has progressed substantially, aiming to understand and predict this hazard. In this work, additional value is given to microseismic monitoring, using it to map temporal changes of seismic velocity in the rock mass that can potentially be associated with stress changes leading to rockbursts. An application of the ensemble Kalman filter is presented for assimilating travel times of seismic P and S waves in a fast and efficient way in order to update the mine's velocity model. Combining sequential Gaussian simulation and ensemble Kalman filter techniques, we were able to monitor the occurrence of velocity changes underground. The proposed approach allows zones to be highlighted where the rock mass is under stress and where potential risk can be expected. The performance of the method was first tested on several synthetic scenarios and, subsequently, on a real 3D case of a deep mine in Canada. The application on the real data set allowed mapping a change in velocity in the area where a rockburst occurred four hours afterwards.
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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 | 0.000 | 0.000 |
| 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".