首页|应用残差网络的微地震事件五分类检测方法

应用残差网络的微地震事件五分类检测方法

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
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.
Five-category detection method for microseismic events based on residual network
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.

microseismic monitoringevent detectiondata augmentationresidual networkdeep learning

潘禹行、田宵、甘兆龙、张雄、张伟

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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

上海佘山地球物理国家野外科学观测研究站,上海 200062

微地震监测 事件检测 数据增广 残差网络 深度学习

广东省地球物理高精度成像技术重点实验室项目江西省自然科学基金&&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上海佘山地球物理国家野外科学观测研究站开放基金

2022B121201000220224BAB21304720224BAB211024SDGD202210SSOP202103

2024

10.13810/j.cnki.issn.1000-7210.2024.03.002

石油地球物理勘探
东方地球物理勘探有限责任公司

石油地球物理勘探

CSTPCD北大核心
影响因子：1.766
ISSN：1000-7210
年,卷(期)：2024.59(3)