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Automatic Identification and Statistical Analysis of Data Steps in Electric Field Measurements from CSES-01 Satellite

Authors :
Jianping Huang
Zongyu Li
Zhong Li
Wenjing Li
Livio Conti
Hengxin Lu
Na Zhou
Ying Han
Haijun Liu
Xinfang Chen
Zhaoyang Chen
Junjie Song
Xuhui Shen
Source :
Remote Sensing, Vol 15, Iss 24, p 5745 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

The spaceborne Electric Field Detector (EFD) is one of the payloads of the China Seismo-Electromagnetic Satellite (CSES-01), which can measure electric field data at near-Earth orbit for investigating fundamental scientific topics such as the dynamics of the top-side ionosphere, lithosphere–atmosphere–ionosphere coupling, and electromagnetic field emissions possibly associated with earthquake occurrence. The Extremely Low-Frequency (ELF) waveform shows anomalous step variations, and this work proposes an automatic detection algorithm to identify steps and analyze their characteristics using a convolutional neural network. The experimental results show that the developed detection method is effective, and the identification performance reaches over 90% in terms of both accuracy and area under the curve index. We also analyze the rate of the occurrence of steps in the three components of the electric field. Finally, we discuss the stability of the statistical results on steps and their relevance to the probe’s function. The research results provide a guideline for improving the quality of EFD data, and further applications in monitoring the low-Earth electromagnetic environment.

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
24
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.3f806eee4fd2477fa7786a0626c9c300
Document Type :
article
Full Text :
https://doi.org/10.3390/rs15245745