应用残差网络的微地震事件五分类检测方法
Five-category detection method for microseismic events based on residual network
潘禹行 1田宵 1甘兆龙 2张雄 3张伟4
作者信息
- 1. 江西省防震减灾与工程地质灾害探测工程研究中心(东华理工大学),江西南昌 330013;广东省地球物理高精度成像技术重点实验室(南方科技大学),广东深圳 518055
- 2. 江西省防震减灾与工程地质灾害探测工程研究中心(东华理工大学),江西南昌 330013
- 3. 江西省防震减灾与工程地质灾害探测工程研究中心(东华理工大学),江西南昌 330013;上海佘山地球物理国家野外科学观测研究站,上海 200062
- 4. 广东省地球物理高精度成像技术重点实验室(南方科技大学),广东深圳 518055
- 折叠
摘要
常规的微地震事件检测方法通常需要人工选取阈值,在处理大量连续记录数据时效率较低,难以适应实时监测的需求.为此,提出一种基于残差网络的微地震事件五分类检测方法,将样本分为噪声、完整的微震事件、只含有P波、只含有S波以及多个微震事件五类.该方法只需将连续记录的波形数据等分,并通过时窗调整获得完整的微震记录.通过一系列数据增广方法实现小规模实际数据样本集的模型训练,模型精度高达99％.将该方法与二分类方法同时应用于微地震监测数据检测,并通过P波、S波到时拾取和震源定位评估检测效果.研究结果表明,基于残差网络的五分类检测方法检测到了更多数量的微震事件,且具有较高的运算效率,满足实时监测的需求.
Abstract
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 mi-croseismic 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.
关键词
微地震监测/事件检测/数据增广/残差网络/深度学习Key words
microseismic monitoring/event detection/data augmentation/residual network/deep learning引用本文复制引用
基金项目
广东省地球物理高精度成像技术重点实验室项目(2022B1212010002)
江西省自然科学基金(20224BAB213047)
&&(20224BAB211024)
江西省防震减灾与工程地质灾害探测工程研究中心开放基金(SDGD202210)
上海佘山地球物理国家野外科学观测研究站开放基金(SSOP202103)
出版年
2024
国家科技期刊平台
NSTL
万方数据