基于时频分析和卷积神经网络的微地震事件检测  

Detection of microseismic events based on time-frequency analysis and convolutional neural network

作　　者：盛立[1] 徐西龙 王维波[1] 高明[1] SHENG Li;XU Xilong;WANG Weibo;GAO Ming(College of Control Science and Engineering in China University of Petroleum(East China),Qingdao 266580,China)

机构地区：[1]中国石油大学(华东)控制科学与工程学院,山东青岛266580

出　　处：《中国石油大学学报（自然科学版）》2021年第5期54-63,共10页Journal of China University of Petroleum(Edition of Natural Science)

基　　金：国家自然科学基金项目(62173343,62073339,62033008);山东省自然科学基金项目(ZR2020YQ49)。

摘　　要：针对传统微地震事件检测方法存在的预处理步骤繁琐、人工干预严重等问题,提出一种基于时频分析和卷积神经网络的微地震事件检测方法。该方法使用实际的油气井水力压裂微地震监测信号作为原始数据,利用S变换提取时频谱构建样本数据集,然后建立卷积神经网络模型对时频谱样本进行特征提取和分类识别。为了验证所提方法的可行性,分别对低信噪比的合成微地震信号,以及实际油井不同类型的地面微地震监测信号进行事件检测。结果表明:该方法可以有效检测包含低信噪比信号及微弱信号在内的多类微地震事件;与短时傅里叶变换、小波变换等其他时频分析方法结合卷积神经网络的算法相比,基于S变换与卷积神经网络的检测方法具有更高的识别准确率与稳定性。Aiming at the problem that traditional microseismic event detection methods have cumbersome pretreatment steps and severe manual intervention,a novel detection method is proposed based on the time-frequency analysis and convolutional neural network(CNN).The actual microseismic signals of oil and gas well hydraulic fracturing is treated as the original data.Then,the sample data set is constructed by using the spectrum extracted by S-transform.Finally,the CNN is constructed to realize the feature extraction and classification recognition of the time-spectrum samples.In order to verify the feasibility of the proposed method,both synthetic microseismic signals with low signal-to-noise ratio(SNR)and different types of surface microseismic signals of oil wells are tested,respectively.The results show that the method can effectively detect multiple types of microseismic events,including low SNR signals and weak signals.Compared with the algorithms combining CNN with other time-frequency analysis methods such as short-time Fourier transform and wavelet transform,the detection method based on S-transform and CNN has higher recognition accuracy and stability.

关 键 词：微地震事件检测 时频分析 S变换 卷积神经网络 

分 类 号：P631[天文地球—地质矿产勘探]