Six-Component Seismic Waves Polarization Vectors Identification Based on Multi-Layer Fully Connected Neural Network
Abstract Using principles of machine learning, we propose a six-component(6C) seismic waves polarization vector identification method based on multi-layer fully connected(MFC) neural network. Firstly, each 5 000 polarization vector data sets for five wave types and noise are obtained by using the mathematical model of polarization vectors of 6C wave types under a series of simulation parameters. 5 000 of them are randomly selected as test sets and the others as training sets. We make a comprehensive comparison for identification performance between MFC neural network and support vector machine(SVM) model. The results show that the MFC neural network model is significantly better than the SVM model both in identifying five(SH and Love waves are treated one type) and six polarization vector types, with the average recognition rate of 99.786% and 87.940%, respectively.
Key words ：
polarization vector identification
six-component seismic waves
multi-layer fully connected neural network
support vector machine
Cite this article:
LIAO Chengwang,PANG Cong,JIANG Yong et al. Six-Component Seismic Waves Polarization Vectors Identification Based on Multi-Layer Fully Connected Neural Network[J]. jgg, 2024, 44(4): 331-335.
LIAO Chengwang,PANG Cong,JIANG Yong et al. Six-Component Seismic Waves Polarization Vectors Identification Based on Multi-Layer Fully Connected Neural Network[J]. jgg, 2024, 44(4): 331-335.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2024/V44/I4/331
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Support Vector Machines
