基于信号时频特征的微震波形识别在岩爆预测中的应用  

Application of microseismic waveform recognition based on signal time-frequency characteristics in rock burst prediction

作　　者：李文旭 陈祖煜 唐春安[3] 苏岩[4] 唐烈先 胡晶[2] 陶磊[4] LI Wenxu;CHEN Zuyu;TANG Chunan;SU Yan;TANG Liexian;HU Jing;TAO Lei(State Key Laboratory of Eco-Hydraulic In Northwest Arid Region,Xi'an University of Technology,Xi'an 710048,China;China Institute of Water Resources and Hydropower Research,Beijing 100048,China;School of Ciril Engineering,Dalian University of Technology,Liaoning 116024,China;Hanjiang-to-Weihe River Water Diversion Project Construction Co.Ltd.,Xi'an 710010,China;School of Mining,University of Science and Technology Liaoning,Anshan 114051,China)

机构地区：[1]西安理工大学省部共建西北旱区生态水利国家重点实验室,陕西西安710048 [2]中国水利水电科学研究院,北京100048 [3]大连理工大学土木工程学院,辽宁大连116024 [4]陕西省引汉济渭工程建设有限公司,陕西西安710010 [5]辽宁科技大学矿业学院,辽宁鞍山114051

出　　处：《中国水利水电科学研究院学报（中英文）》2022年第6期523-531,556,共10页Journal of China Institute of Water Resources and Hydropower Research

基　　金：陕西省引汉济渭工程建设有限公司科研项目(YHJW-D-70);陕西省联合研究基金资助项目(2021JLM-53)。

摘　　要：在深埋地下工程施工中,需要通过监测采集微震信号分析施工过程中的岩爆风险,由于现场干扰因素多,数据中混入了大量冗余信号,大大影响了岩爆预测的效率。为有效识别出岩石的破裂信号,本文采用快速傅里叶变换,对比分析了微震?岩爆信号与其它无效信号的频率特征,采用多输入的卷积神经网络方法,建立了基于信号时频特征的微震波形识别模型,实现了对微震?岩爆信号的有效识别。基于引汉济渭秦岭输水隧洞的微震监测资料,采用3770个波形对模型进行了测试,模型识别精度可达96.1%。模型对比了不同输入方式对预测结果的影响,针对随机挑选的100次微震事件和100次无效事件,结果表明:采用信号的时频特征作为输入,模型比单纯采用时域或频域特征具有更高的精度。In the construction of deep-buried underground engineering,it is necessary to analyze the rock burst risk in the construction process by monitoring and collecting mic roseismic signals.Due to the multiple interference factors on site,a large number of redundant signals are mixed into the data,which greatly affects the efficiency of rock burst prediction.To effectively identify the rock burst signal,this paper uses fast Fourier transform to analyze frequency characteristics of mic rotremor/rock burst with other invalid signals,employs multiple input convolutional neural network method to construct the micro wave shape recognition model based on the signal timefrequency characteristics,and realizes the effective identification of microtremor/rock burst signal.Based on the microseismic monitoring data of Qinling water conveyance tunnel of Hanjiang to Weihe River valley water diversion project,3770 waveforms are used to test the model,and the model recognition accuracy can reach 96.1%.The model compares the influence of different input methods on the prediction results,and for the randomly selected 100 microseismic events and 100 invalid events,the results show that the model with the time-frequency charac-teristics of the signal as input has higher accuracy than with the time-domain or frequency-domain characteristics.

关 键 词：地下工程 微震监测 波形识别 频域 卷积神经网络 

分 类 号：P631.4[天文地球—地质矿产勘探]