Publication Date:
2022-03-22
Description:
Low Earth orbit satellites collect and study information on changes in the ionosphere,
which contributes to the identification of earthquake precursors. Swarm, the European Space
Agency three-satellite mission, has been launched to monitor the Earth geomagnetic field, and has
successfully shown that in some cases it is able to observe many several ionospheric perturbations
that occurred as a result of large earthquake activity. This paper proposes the SafeNet deep learning
framework for detecting pre-earthquake ionospheric perturbations. We trained the proposed model
using 9017 recent (2014–2020) independent earthquakes of magnitude 4.8 or greater, as well as the
corresponding 7-year plasma and magnetic field data from the Swarm A satellite, and excellent
performance has been achieved. In addition, the influence of different model inputs and spatial
window sizes, earthquake magnitudes, and daytime or nighttime was explored. The results showed
that for electromagnetic pre-earthquake data collected within a circular region of the epicenter and
with a Dobrovolsky-defined radius and input window size of 70 consecutive data points, nighttime
data provided the highest performance in discriminating pre-earthquake perturbations, yielding an
F1 score of 0.846 and a Matthews correlation coefficient of 0.717. Moreover, SafeNet performed well in
identifying pre-seismic ionospheric anomalies with increasing earthquake magnitude and unbalanced
datasets. Hypotheses on the physical causes of earthquake-induced ionospheric perturbations are
also provided. Our results suggest that the performance of pre-earthquake ionospheric perturbation
identification can be significantly improved by utilizing SafeNet, which is capable of detecting
precursor effects within electromagnetic satellite data.
Description:
Published
Description:
5033
Description:
7T. Variazioni delle caratteristiche crostali e "precursori"
Description:
JCR Journal
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
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