Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145959
Title: Identification of electromagnetic pre-earthquake perturbations from the DEMETER data by machine learning
Authors: Xiong, Pan
Long, Cheng
Zhou, Huiyu
Battiston, Roberto
Zhang, Xuemin
Shen, Xuhui
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Xiong, P., Long, C., Zhou, H., Battiston, R., Zhang, X., & Shen, X. (2020). Identification of electromagnetic pre-earthquake perturbations from the DEMETER data by machine learning. Remote Sensing, 12(21), 3643-. doi:10.3390/rs12213643
Journal: Remote Sensing
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.
URI: https://hdl.handle.net/10356/145959
ISSN: 2072-4292
DOI: 10.3390/rs12213643
Rights: © 2020 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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