• 固体地球物理及空间物理学（大气、行星、地球动力学、重磁电及地震学、地热学） •

地震和爆破事件源波形信号的卷积神经网络分类研究

1. 广西师范大学计算机科学与信息工程学院,桂林 541004
• 收稿日期:2017-10-10 修回日期:2018-07-11 出版日期:2018-08-20 发布日期:2018-09-13
• 通讯作者: 黄汉明 E-mail:huanghm@gxnu.edu.cn
• 作者简介:陈润航,男,1990年生,硕士研究生,主要研究方向为信号处理与模式识别.(E-mail: crunhang@163.com)
• 基金资助:
国家自然科学基金资助((41264001))

Study on the discrimination of seismic waveform signals between earthquake and explosion events by convolutional neural network

Run-hang CHEN,Han-ming HUANG(),Hui-min CHAI

1. College of Computer Science and Information Engineering, Guangxi Normal University, Guilin 541004, China
• Received:2017-10-10 Revised:2018-07-11 Online:2018-08-20 Published:2018-09-13
• Contact: Han-ming HUANG E-mail:huanghm@gxnu.edu.cn

Abstract:

This paper firstly extracts Mel Frequency Cepstrum Coefficient (MFCC) map from seismic source waveform, and then applies the Convolutional Neural Network (CNN) to discriminate seismic waveform signals between earthquake and explosion events. The events are 72 earthquake and 101 man-made explosion events in the Beijing region and nearby, and the waveform signal used to extract MFCC map is the vertical component of the 3 components of the waveform in each observation station. For any selected waveform (vertical component of 3 components) of all observation stations of anyone event, whether the waveform being messed with noises is judged by the same criterion, only the waveform that is not messed with noise is selected. Otherwise the waveform is discarded. Although there are 107 observation stations for each event, certainly there are 107 vertical components, after noise-messing waveforms being discarded, there are maybe only several to dozens of vertical components remaining for one event. Then, the MFCC map of the waveform that is left without noise or with small noise are extracted, and the MFCC map are used as the input of the CNN, and the output of the CNN is the seismic source type of the waveform (earthquake or explosion). If a single waveform is taken as a recognition unit, and 5-fold cross validation test was adopted, the average recognition rate is 95.78%. Using the single waveform in the training set as the recognition unit, extracting MFCC map, adopting CNN training strategy, a classifier is formulated for discriminating earthquake and explosion events. In testing, multi waveform signals from the same event are not usually classified consistently for the same event, it is likely that some are classified as earthquake events, some are classified as explosion events. If the event is regarded as the recognition unit, for the same event, more than half of the waveforms are identified as a type of event, this event is classified as this type of event, and then the correct recognition rate is 97.1%. The experimental results show that the convolutional neural network exhibits excellent performance in earthquake and explosion event recognition. This shows that MFCC and convolutional neural networks can be used to identify earthquake and explosion events, and deep learning theory may also be worth further study in seismic signal processing.