Five-category detection method for microseismic events based on residual network

被引：0
|
作者：
Pan Y. [1 ,2 ]
Tian X. [1 ,2 ]
Gan Z. [1 ]
Zhang X. [1 ,3 ]
Zhang W. [2 ]
机构：
[1] Engineering Research Center for Seismic Disaster Prevention and Engineering Geological Disaster Detection of Jiangxi Province, East China University of Technology, Jiangxi, Nanchang
[2] Guangdong Provincial Key Laboratory of Geophysical High⁃Resolution Imaging Technology, Southern University of Science and Technology, Guangdong, Shenzhen
[3] Shanghai Sheshan National Geophysical Observatory, Shanghai
来源：
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2024年 / 59卷 / 03期
关键词：
data augmentation; deep learning; event detection; microseismic monitoring; residual network;
D O I：
10.13810/j.cnki.issn.1000-7210.2024.03.002
中图分类号：
学科分类号：
摘要：
Conventional detection methods for microseismic events usually require manual selection of the threshold. They are inefficient when processing a large amount of continuously recorded data and fail to meet the needs of real-time monitoring. This study proposes a five-category detection method for microseismic events based on a residual network，which divides samples into five categories：noise，microseismic events，only P waves，only S waves，and multiple microseismic events. This method only needs to equally divide the continuously recorded waveform data and obtain a complete microseismic record by shifting time windows. Through a series of data augmentation methods，the model of a small set of actual data samples is trained，and the model accuracy is as high as 99%. This method and the binary classification method are used to detect microseismic monitoring data at the same time，and the detection effect is evaluated through P-wave and S-wave arrival time picking and source location. The research results show that the five - category detection method based on the residual network has greatly improved the detection quantity of microseismic events，and it has high computing efficiency，which can meet the needs of real-time monitoring. © 2024 Editorial office of Oil Geophysical Prospecting. All rights reserved.
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页码：392 / 403
页数：11
相关论文
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