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Record W4386525352 · doi:10.56952/arma-2023-0284

Developing a Velocity Model for an Underground Coal Mine Using a Compressed Load Column Seismic Source

2023· article· en· W4386525352 on OpenAlexaboutno aff
Shawn Boltz, Derrick Chambers, S. Sbai, P. Janson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysics and Sensor Technology
Canadian institutionsnot available
Fundersnot available
KeywordsCoal miningGeophoneLongwall miningMining engineeringGeologyVertical seismic profileCoalUnderground mining (soft rock)SeismologyInduced seismicityEngineering

Abstract

fetched live from OpenAlex

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).

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.069
GPT teacher head0.269
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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