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Title: Pre-earthquake ionospheric perturbation identification using CSES data via transfer learning
Authors: Xiong, Pan
Long, Cheng
Zhou, Huiyu
Battiston, Roberto
De Santis, Angelo
Ouzounov, Dimitar
Zhang, Xuemin
Shen, Xuhui
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Xiong, P., Long, C., Zhou, H., Battiston, R., De Santis, A., Ouzounov, D., Zhang, X. & Shen, X. (2021). Pre-earthquake ionospheric perturbation identification using CSES data via transfer learning. Frontiers in Environmental Science, 9, 779255-.
Journal: Frontiers in Environmental Science 
Abstract: During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky radius and an input sequence length of 20 consecutive observations during night time. We further explore a transferring learning approach, which initially trains the model with the larger Electro-Magnetic Emissions Transmitted from the Earthquake Regions (DEMETER) dataset, and then tunes the model with the CSES dataset. The transfer-learning performance is substantially higher than that of direct learning, yielding a 12% improvement in the F1 score and a 29% improvement in the MCC value. Moreover, we compare the proposed model SeqNetQuake with other five benchmarking classifiers on an independent test set, which shows that SeqNetQuake demonstrates a 64.2% improvement in MCC and approximately a 24.5% improvement in the F1 score over the second-best convolutional neural network model. SeqNetSquake achieves significant improvement in identifying pre-earthquake ionospheric perturbation and improves the performance of earthquake prediction using the CSES data.
ISSN: 2296-665X
DOI: 10.3389/fenvs.2021.779255
Schools: School of Computer Science and Engineering 
Rights: © 2021 Xiong, Long, Zhou, Battiston, De Santis, Ouzounov, Zhang and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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