基于S谱能量曲线与卷积神经网络的天然地震与爆破事件分类识别  

Earthquake and artificial blasting identification based on S-spectrum energy curve and convolutional neural networks

作　　者：孟娟 李亚南 高强 Meng Juan;Li Yanan;Gao Qiang(School of Electronic Science and Control Engineering,Institute of Disaster Prevention,Hebei Sanhe 065201,China)

机构地区：[1]防灾科技学院电子科学与控制工程学院,河北三河065201

出　　处：《地震学报》2025年第2期232-241,共10页Acta Seismologica Sinica

基　　金：河北省廊坊市科技支撑计划项目(2024011008);中央高校基本科研业务费研究生科技创新基金项目(ZY20240329)联合资助。

摘　　要：以震级为ML1.3—3.0的1万2936条人工爆破微震记录和1万3215条天然微震波形为研究对象,对其原始地震波形进行1—30 Hz带通滤波以去除长周期干扰,并基于长短时窗均值比(STA/LTA)算法进行P波识别与筛选。对处理后的地震波形数据进行S变换,获取其S谱能量曲线,然后将S谱能量曲线图转换为32×32像素的灰度特征图,并将其作为卷积神经网络的输入进行训练,基于训练好的模型进行10折交叉测试验证。结果显示地震与爆破事件的分类识别准确率高达97.80%,表明利用S谱能量曲线能较好地识别天然地震与人工爆破。With the improvement of earthquake monitoring capabilities and the surge of monitoring data,researches on seismology has entered the era of big data.Especially with the increase of mining blasting,engineering demolition,military construction and other activities,seismic stations will collect a large number of natural and artificial blasting waveforms.Accurately and quickly identifying artificial blasting and natural earthquakes from waveforms has become one of the focuses of earthquake warning and prediction research.Numerous scholars have conducted in-depth researches on earthquake event classification and recognition.The use of convolutional neural network(CNN)technology for earthquake event detection and classification is currently one of the research hot-spots,but one of the key challenges is how to capture the different features of artificial blasting and natural earthquakes.In order to further study the application of CNN in the field of earthquake event automatic detection and improve the efficiency of event automatic detection,a study was conducted on the classification and identification of natural earthquakes and blasting events based on CNN,with 12936 artificial blasting micro-seismic records and 13215 natural micro-seismic records with magnitude M_(L)1.3−3.0 as the research objects.Firstly,the seismic waveforms are preprocessed.The original seismic waveforms are filtered using a band-pass filter with a range of 1−30 Hz to remove long-period interference components,resulting in distinct P-and S-wave records.Based on this,P-wave identification is performed using short-term/long-term average(STA/LTA)algorithm,with STA duration set as 0.2 seconds,LTA duration set as 1 second,and threshold size set as 2.The waveforms from 20 seconds before the first arrival time to 100 seconds after the last arrival time were taken as the screening result for this record,resulting in 12132 effective natural earthquake screening records and 11721 artificial blasting screening records.Secondly,the S-transform is applied

关 键 词：人工爆破 天然地震 卷积神经网络(CNN) S变换 分类识别 

分 类 号：P315.31[天文地球—地震学] P315.63[天文地球—固体地球物理学]