摘要
本文以西昌台阵观测的8 321次近震数据为例,详细介绍了利用深度卷积神经网络检测地震的数据处理流程,包括数据预处理、模型训练、波形长度、网络层数、学习率和概率阈值等关键参数对检测结果的影响,并将训练得到的最优模型,应用于事件波形和连续波形的检测。研究表明,数据预处理和数据增强可以提升模型的检测精度和抗干扰能力。用于模型训练的波形窗口长度可近似于S-P到时差的最大值。不同网络层数(5—8层)的检测结果差别不大。对于地震检测,学习率设为10-4—10-3较为合适。卷积神经网络检测出的地震数量与选择的概率阈值有关,通过绘制精确率-召回率变化曲线,可以为选择合适的概率阈值提供参考。本文为进一步利用深度学习算法提高地震检测效果提供了参考。
Earthquake detection is the key step of automatic processing such as earthquake quick report and earthquake early warning. In recent years,the use of deep learning algorithm to detect earthquakes has developed rapidly. However,there are few detailed researches on data processing flow and neural network parameter optimization. Taking 8 321 near earthquake data observed by Xichang array as an example,this paper introduces in detail the data processing flow of detecting earthquakes by using the deep convolution neural network,such as data preprocessing,model training,waveform length,network layers,learning rate and probability threshold on the detection results. Then we detect the continuous waveform with the optimal model. Our research shows that data preprocessing,data augmentation can improve the detection accuracy and anti-interference ability of the model. The length of waveform window used for model training can be approximated to the maximum value from arrival time difference between S-and P-wave. The detection results of different network layers(5—8 layers) are similar. For seismic detection,it is more appropriate to set the learning rate as 10-4—10-3. The earthquakes detected by convolution neural network are related to the probability threshold. By drawing the tradeoff curve of precision with recall rate,it can provide a reference for selecting the appropriate probability threshold. This paper provides an important reference for further study of earthquake detection with deep learning algorithm.
作者
周本伟
范莉苹
张龙
李珀任
房立华
Zhou Benwei;Fan Liping;Zhang Long;Li Poren;Fang Lihua(Institute of Geophysics,China Earthquake Administration,Beijing 100081,China)
出处
《地震学报》
CSCD
北大核心
2020年第6期669-683,I0001,共16页
Acta Seismologica Sinica
基金
国家重点研发计划(2018YFC1503400)
国家自然科学基金(41774067)
中国地震局地球物理研究所基本科研业务费专项(DQJB20X07、DQJB19B07)联合资助。
关键词
深度学习
卷积神经网络
地震检测
模型训练
西昌台阵
deep learning
convolutional neural network
earthquake detection
model training
Xichang array
