Machine learning-based microseismic catalog and passive seismic tomography evaluating the effect of grouting in Zhangji coal mine, China

Fault grouting is important in preventing groundwater inrush into coal mines from aquifers underlying the coal seams. However, evaluating the effect of grouting in the coal mine is difficult and expensive; hence, the region is sparsely explored. Therefore, we propose a more economical and efficient...

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Bibliographic Details
Published inApplied geophysics Vol. 20; no. 2; pp. 167 - 175
Main Authors Qian, Jia-Wei, Anyiam, Uzonna Okenna, Wang, Kang-Dong
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023
Springer Nature B.V
Subjects
Aquifers
Artificial neural networks
Catalogues
Coal
Coal mines
Coal mining
Earth and Environmental Science
Earth Sciences
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Groundwater
Grouting
Learning algorithms
Machine learning
Microseisms
Monitoring systems
Neural networks
Passive Seismic Tomography
Real time
Seismic activity
Seismic tomography
Seismic velocities
Tomography
Velocity
Work face
China
velocity anomaly
passive seismic tomography
microseismic monitoring
grouting
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Summary:Fault grouting is important in preventing groundwater inrush into coal mines from aquifers underlying the coal seams. However, evaluating the effect of grouting in the coal mine is difficult and expensive; hence, the region is sparsely explored. Therefore, we propose a more economical and efficient method for investigating grouting in the coal mine, which utilizes a real-time microseismic monitoring system. A system is an integrated approach involving a deep neural network phase picker method, grid search location method, and double-difference seismic tomography method for enhanced real-time processing of the microseismic data of 1613A working face in Zhangji coal mine, China. The velocity structure of the 1613A working face has been inverted using the microseismic events in September 2020. We found that the grouting treatment area is considerably perturbed during mining, with the 50-m area below the mining floor characterized by a high seismic velocity of up to 4.3 km/ s, showing optimal grouting. The checkerboard resolution test shows that the seismic velocity results in concern have high reliability. The effect of grouting is well evaluated by machine learning-based microseismic catalog and passive seismic tomography.
ISSN:1672-7975
1993-0658
DOI:10.1007/s11770-023-1056-5