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

• Published in: Applied geophysics Vol. 20; no. 2; pp. 167 - 175
• Main Authors: Qian, Jia-Wei, Anyiam, Uzonna Okenna, Wang, Kang-Dong
• Format: Journal Article
• Language: English
• 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

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...