石油地球物理勘探2024，Vol.59Issue(3)：392-403,12.DOI:10.13810/j.cnki.issn.1000-7210.2024.03.002
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
摘要
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分类
天文与地球科学引用本文复制引用
潘禹行,田宵,甘兆龙,张雄,张伟..应用残差网络的微地震事件五分类检测方法[J].石油地球物理勘探,2024,59(3):392-403,12.基金项目
本项研究受广东省地球物理高精度成像技术重点实验室项目"基于人工智能的地面微地震事件成像方法研究"(2022B1212010002)、江西省自然科学基金项目"基于人工智能的江西地区天然地震和非天然地震事件识别方法研究"(20224BAB213047)及"多台地震实时监测的泛化神经网络及其在赣北地区的应用"(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项目"基于交叉双差算法的震源位置和三维速度结构联合反演方法研究"(SDGD202210)、上海佘山地球物理国家野外科学观测研究站开放基金项目"基于深度学习的地震监测和预警方法在川滇地区的应用研究"(SSOP202103)联合资助. （2022B1212010002）
