Microseismic source location using deep learning:A coal mine case study in China

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外文摘要：Microseismic source location is crucial for the early warning of rockburst risks.However,the conven-tional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial infor-mation but struggle with complex microseismic sequence.Combining the FCNN with the long short-term memory(LSTM)network enables better time-series signal classification by integrating multi-scale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.

外文关键词：

Microseismic source locationRockburstDeep learningIntelligent early warning

作者：

Yue Song、Enyuan Wang、Hengze Yang、Chengfei Liu、Baolin Li、Dong Chen

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作者单位：

School of Safety Engineering,China University of Mining and Technology,Xuzhou,221116,China

State Key Laboratory of Coal Mine Disaster Prevention and Control,China University of Mining and Technology,Xuzhou,221116,China

School of Environment and Safety Engineering,North University of China,Taiyuan,030051,China

State Key Laboratory for Geomechanics & Deep Underground Engineering,China University of Mining Technology,Xuzhou,221116,China

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基金：

Fundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

项目编号：

2022XSCX355193400752104230

出版年：

2024

DOI：

10.1016/j.jrmge.2024.02.014

岩石力学与岩土工程学报(英文版)

中国科学院武汉岩土力学所中国岩石力学与工程学会武汉大学

岩石力学与岩土工程学报(英文版)

CSTPCD

影响因子：0.404

ISSN：1674-7755

年,卷(期)：2024.16(9)