Microseismic event locations and source mechanisms using dominant guided waves recorded in an underground potash mine
Bibliographic record
Abstract
ABSTRACT Microseismic event locations and moment tensors (MT) in underground mines can provide insights into the subsurface deformation and current state of stress. However, reliable estimation of these source parameters is rather challenging due to high-frequency waveforms and a low signal-to-noise ratio for negative magnitude events. We study microseismicity in an underground potash mine in Saskatchewan, Canada, recorded between 1 March and 30 June 2021, by a network of broadband seismometers. The active mining is carried out in low-velocity evaporites at depths of approximately 1 km below the ground level. The theoretical dispersion curves show that guided waves in the form of leaky P and P-SV/SH normal modes can exist in a 1D velocity model representing mine geology. These guided waves are detected as high-energy dispersive arrivals on the seismograms recorded at the underground receivers. We locate the events using the arrival times of the guided waves and their mean group velocities. Most (approximately 80%) of the detected events cluster around the mine layout between depths of 0.95 to 1.05 km. Next, we compute MT for 92 events using waveforms of guided phases. The MT show nondouble-couple components with only 28 events having double-couple percentages greater than 50%. These events occur near mined-out cavities with source mechanisms corresponding to the layer delamination in the roof and floor or pillar yield related to the closure of cavities. No abnormal microseismicity is detected away from the mine levels in the more competent carbonate rocks above or below the evaporite formations. Thus, guided waves enable the detection of microseismic events up to large distances and can provide high-resolution event locations and MT inversion. These can be interpreted in the context of local geology and mining activities to identify the dominant factors affecting microseismicity.
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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 | 0.000 |
| 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".