Identification of Electromagnetic Pre-Earthquake Perturbations from the DEMETER Data by Machine Learning

Pan Xiong, Cheng Long, Huiyu Zhou, Roberto Battiston, Xuemin Zhang, Xuhui Shen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

The low-altitude satellite DEMETER recorded many cases of ionospheric perturbations observed on occasion of large seismic events. In this paper, we explore 16 spot-checking classification algorithms, among which, the top classifier with low-frequency power spectra of electric and magnetic fields was used for ionospheric perturbation analysis. This study included the analysis of satellite data spanning over six years, during which about 8760 earthquakes with magnitude greater than or equal to 5.0 occurred in the world. We discover that among these methods, a gradient boosting-based method called LightGBM outperforms others and achieves superior performance in a five-fold cross-validation test on the benchmarking datasets, which shows a strong capability in discriminating electromagnetic pre-earthquake perturbations. The results show that the electromagnetic pre-earthquake data within a circular region with its center at the epicenter and its radius given by the Dobrovolsky’s formula and the time window of about a few hours before shocks are much better at discriminating electromagnetic pre-earthquake perturbations. Moreover, by investigating different earthquake databases, we confirm that some low-frequency electric and magnetic fields’ frequency bands are the dominant features for electromagnetic pre-earthquake perturbations identification. We have also found that the choice of the geographical region used to simulate the training set of non-seismic data influences, to a certain extent, the performance of the LightGBM model, by reducing its capability in discriminating electromagnetic pre-earthquake perturbations.

Original languageEnglish
Article number3643
Number of pages27
JournalRemote Sensing
Volume12
Issue number21
DOIs
Publication statusPublished - 06 Nov 2020
Externally publishedYes

Bibliographical note

Funding Information:
This work is supported in part by the National Key R&D Program of China under Grant No. 2018YFC1503505, and in part by the Special Fund of the Institute of Earthquake Forecasting, China Earthquake Administration under Grant 2020IEF0510 and Grant 2020IEF0705.

Funding Information:
Funding: This work is supported in part by the National Key R&D Program of China under Grant No. 2018YFC1503505, and in part by the Special Fund of the Institute of Earthquake Forecasting, China Earthquake Administration under Grant 2020IEF0510 and Grant 2020IEF0705.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • DEMETER satellites
  • Earthquake
  • Electromagnetic field
  • Machine learning
  • Seismic precursors

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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