Development of a Deep Neural Network Model for the Relocation of Mining-Induced Seismic Event

被引:0
|
作者
Wang, Chenlu [1 ]
Shen, Luyi [1 ]
机构
[1] China Univ Geosci, Fac Engn, Wuhan 430074, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 16期
关键词
microseismicity; source relocation; deep learning; joint inversion; INDUCED MICROSEISMICITY; EARTHQUAKE; LOCATION; MINES;
D O I
10.3390/app14166911
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The precise relocation of seismic events is critical for many engineering projects. Swarms of minor or micro earthquakes typically reveal stress concentration and spots of greater seismic hazards. Particularly in the context of deep underground mining, advanced techniques that can accurately relocate microseismicities are urgently in demand. Here, we developed a neural network-based modeling training method that can precisely relocate seismicities and invert for velocities at the same time, with preconfigured receiver network locations. Our model can be iteratively improved with field recorded data. We showed that, with roughly eight iterations, we can reasonably resolve for the earthquake locations for both clusters of events, namely spatially distributed with linear pattern or randomly scattered. Our initially trained model, which only focused on events that had a linear distribution pattern, was used as the base for the training of the subsequent models which could better resolve for randomly scattered event locations. Although we stopped at the eighth iteration, the process reported here can be continued, as the model will have a better performance with more iterations.
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页数:16
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