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Pre-earthquake ionospheric perturbation identification using CSES data via transfer learning

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journal contribution
posted on 17.11.2021, 09:28 by P Xiong, C Long, Huiyu Zhou, R Battiston, A De Santis, D Ouzounov, X Zhang, X Shen
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.

Funding

This work is funded by the National Key Research and Development of China under Grant No. 2018YFC1503505. This work is also supported by the LIMADOU-Science under Grant No. 2020-31-HH.0, a project funded by the Italian Space Agency (ASI), and INGV Further and MiUR Pianeta Dinamico-Working Earth Project.

History

Citation

Front. Environ. Sci. 9:779255. doi: 10.3389/fenvs.2021.779255

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Version

VoR (Version of Record)

Published in

Frontiers in Environmental Science

Volume

9

Pagination

779255

Publisher

Frontiers Media

eissn

2296-665X

Acceptance date

21/10/2021

Copyright date

2021

Available date

17/11/2021

Language

en