Developing a Velocity Model for an Underground Coal Mine Using a Compressed Load Column Seismic Source
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Bibliographic record
Abstract
ABSTRACT Induced seismicity is common in underground mining, particularly in mines that cave by design, such as longwall coal mining. Seismic monitoring is one of the few remote sensing technologies that provide an understanding of a mine's response to extraction. However, its effectiveness usually depends on understanding event locations in relation to the mine workings. A significant source of uncertainty in event locations is the velocity model: a description of the speed at which seismic energy propagates. A nondestructive, repeatable compressed load column seismic source (CLCSS) was developed for velocity model calibration in underground coal mines. This paper describes the CLCSS and its application at a longwall coal mine. Signals from the CLCSS were detected up to 950 m epicentrally (1,100 m hypocentrally) from the source. Using the ground-truthed signals from the seismic source, we estimate a velocity model for locating events from a surface geophone array. Model performance is evaluated by relocating mining-induced events with well-constrained locations. A three-layer model with P-wave velocities ranging from 3.6 km/s to 4.4 km/s and a VP/VS ratio of 2.1 performed best with location errors of approximately 100 m, which is sufficient for many applications of seismic monitoring in coal mines. INTRODUCTION Coal bursts—violent dynamic failures which cause damage to mine openings—were first documented in European coal mines well before World War I (Guan et al., 2009) and continue to threaten underground coal miners around the world. For example, approximately 280 significant bursts occurred in U.S. coal mines between 1983 and 2017, seven of which resulted in fatalities (Mark, 2018). In China, over 200 coal mines have reported bursts cumulatively resulting in over 1,000 injuries and 100 fatalities in the past 10 years (Rong et al., 2022). Poland's Upper Silesian Coal Basin has experienced over 100 significant events, some of which resulted in injuries and fatalities (Mutke et al., 2015). Several other countries have also reported coal mine bursts, including Japan, Australia, India, France, South Africa, Czechoslovakia, Canada, Germany, and Russia (Lama and Bodziony, 1998). Although bursting mechanisms are not well understood and vary considerably, studies have identified risk factors including depth of cover or rapid changes in topographic relief, thick brittle strata near the coal seam, inadequately designed pillars, multi-seam interactions, and a variety of other mining and geological factors (Mark and Guana, 2016).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it