U型神经网络结合BiLSTM网络的地震相识别系统
Research on Seismic Phase Recognition System Using U-shaped Neural Network Combined with BiLSTM Network
王天哲1
作者信息
- 1. 中国地质大学(北京),北京 100083
- 折叠
摘要
以BiLSTM网络为基础识别框架,通过在BiLSTM网络结构中引入U型卷积神经网络进行改进,并采用改进的BiLSTM网络对地震相进行识别,实现了地震P波和S波震相的精确识别.仿真结果表明:该方法可有效、准确地识别地震P波和S波震相,平均识别正确率为 90.01%,平均漏检率和均方根误差分别为 11.00%和 0.23,相较于BiLSTM网络以及常用地震相识别MEA-BP神经网络模型和CNN模型,该方法对地震相的识别精度更高,具有明显的优越性,为实现地震相的精确识别提供了参考.
Abstract
To improve the accuracy of seismic phase identification,a deep learning-based seismic phase identification method is proposed.This method is based on the BiLSTM network recognition framework,improved by introducing a U-shaped convolutional neural network into the BiLSTM network structure.The improved BiLSTM network was used to identify seismic phases,achieving accurate identification of seismic P-wave and S-wave phases.The simulation results show that this method can effectively and accurately identify seismic P-wave and S-wave phases,with an average recognition accuracy of 90.01%,an average missed detection rate of 11.00%,and a root mean square error of 0.23.Compared with BiLSTM network,commonly used seismic phase recognition MEA-BP neural network models,and CNN models,this method has higher recognition accuracy for seismic phases and obvious advantages,providing a reference for achieving accurate identification of seismic phases.
关键词
深度学习/地震相识别/BiLSTM网络/U型卷积神经网络Key words
deep learning/seismic phase identification/BiLSTM network/U-shaped convolutional neural network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据