Classifying the Records of DAS Using Neural Network
Session: Recent Development in Ultra-Dense Seismic Arrays with Nodes and Distributed Acoustic Sensing [Poster]
Type: Poster
Date: 4/20/2021
Presentation Time: 04:15 PM Pacific
Description:
One advantage of the Distributed Acoustic Sensing technology is ultra-dense spatial sampling, which helps to reduce wavefield alias. However, the data volume also significantly increases to terabytes per day and it is difficult to process by traditional approaches including automatic and manual ones. One of the typical data procedures of continuous seismic data is event classification. The relative weak earthquake signal is strongly contaminated by the strong traffic noise in urban areas. Therefore, a new automatic event classification method based on the neural network was developed to detect earthquake signals recorded by a telecom fiber-optic array in a city. The neural network was trained by a dataset including more than 60 local events beneath the DAS array. Then, it was successfully used into the 7-days dataset and detected low signal-noise-ratio microearthquakes.
Presenting Author: Hao Lv
Student Presenter: Yes
Authors
Hao Lv Presenting Author lh17@mail.ustc.edu.cn State Kay Laboratory of Geodesy and Earth’s Dynamics, Academy of Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China (Mainland) |
Xiangfang Zeng Corresponding Author zengxf@whigg.ac.cn State Kay Laboratory of Geodesy and Earth’s Dynamics, Academy of Precision Measurement Science and Technology, Chinese Academy of Sciences |
Zhenghong Song zhhsong6@mail.ustc.edu.cn State Kay Laboratory of Geodesy and Earth’s Dynamics, Academy of Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China (Mainland) |
Feng Bao baofeng@whigg.ac.cn State Kay Laboratory of Geodesy and Earth’s Dynamics, Academy of Precision Measurement Science and Technology, Chinese Academy of Sciences |
Rongbin Lin robin_lin@163.com State Kay Laboratory of Geodesy and Earth’s Dynamics, Academy of Precision Measurement Science and Technology, Chinese Academy of Sciences |
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Classifying the Records of DAS Using Neural Network
Category
Recent Development in Ultra-Dense Seismic Arrays with Nodes and Distributed Acoustic Sensing