LSTM-Autoencoder Network for the Detection of Seismic Electric Signals-Reference-Cited by-同舟云学术

LSTM-Autoencoder Network for the Detection of Seismic Electric Signals

Published:2022 Issue: Volume:60 Page:1-12
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Xue Jiyan¹^ORCID,Huang Qinghua¹^ORCID,Wu Sihong¹^ORCID,Nagao Toshiyasu²

Affiliation:

1. Department of Geophysics, School of Earth and Space Sciences, Peking University, Beijing, China

2. Institute of Oceanic Research and Development, Tokai University, Shizuoka, Japan

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09796472.pdf?arnumber=9796472

Reference60 articles.

1. PhaseNet: A deep-neural-network-based seismic arrival-time picking method;zhu;Geophys J Int,2019

2. FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation

3. An effective evaluation function for ICA to separate train noise from telluric current data;koganeyama;Proc 4th Int Symp Independ Compon Anal Blind Signal Separat (ICA),2003

4. A regionally observed simultaneous pre-seismic geoelectric potential change in Nagano prefecture, Japan;yamaguchi;Bull Inst Ocean Res Develop Tokai Univ,2000

5. Identifying long-range correlated signals upon significant periodic data loss

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