# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 6 Conclusions  
This study proposed the SafeNet deep learning framework for pre-earthquake ionospheric perturbation identification. SafeNet was trained and tested using 9017 independent earthquakes of magnitude 4.8 and above that occurred from April 2014 to April 2020, and the corresponding plasma and magnetic field data from the Swarm A satellite for about 6 years. The results indicated that electromagnetic pre-earthquake data within a circular region centered on the epicenter and with a radius given by the Dobrovolsky formula, with a model input window size of 70 consecutive points and nighttime sequence data, yielded the best performance in discriminating electromagnetic pre-earthquake perturbations, with an F1 score of 0.846 and an MCC value of 0.717. The study also concluded that the larger the magnitude of the earthquake, the better the performance of the SafeNet model in identifying possible pre-earthquake ionospheric anomalies. The results also suggest that the SafeNet model achieves a good performance for probable pre-seismic perturbation identification on the unbalanced dataset. In addition, based on constraints from this study, we proposed a new hypothesis on the physical mechanisms of earthquake-induced ionospheric perturbations. In order to have a better understanding of the lithosphere, atmosphere, and ionosphere coupling mechanisms, to study pre-earthquake anomalies using electromagnetic satellites, the analysis of the spatial-temporal correlation of multi-sphere and multi-parameters by a remote sensing technique before earthquakes occur has become a hot research topic in recent years. However, it is difficult to match the different parameters and data of each sphere in both time and space. The existing multi-parameter earthquake studies are mainly focused on specific earthquakes or specific remote sensing parameters, have not sufficiently considered the other remote-sensing parameters of other spheres, and have not formed a complete chain of multi-parameter correlation analyses. The deep learning technology can overpass such limitations, combining the remote sensing parameters of multiple spheres in time and space and carrying out the analysis based on a consistent spatial-temporal framework, which could provide global earthquake cases and effectively explain the earthquake coupling mechanism models, and also expand the current tools for earthquake monitoring, providing new perspectives for earthquake prediction. The following are available online at [[https://www.mdpi.com/article/10.3390/rs13245033/s1](https://www.mdpi.com/article/10.3390/rs13245033/s1)]([https://www.mdpi.com/article/10.3390/rs13245033/s1](https://www.mdpi.com/article/10.3390/rs13245033/s1)), Text S1. Swarm Alpha nighttime tracks before the M7.9 Sumatra 2 March 2016 earthquake. Text S2. Swarm Alpha nighttime tracks before the M7.8 Ecuador 16 April 2016 earthquake. Text S3. Performance metrics. Text S4. The SafeNet model structure. Figure S25. The flowchart of the proposed deep learning framework. Table S1. Search space of parameters for the SafeNet model. Conceptualization, X.Z. and X.S. ; data curation, P.X. ; investigation, D.M., A.D.S., and X.Z. ; methodology, P.X. and A.D.S. ; project administration, A.D.S. and X.S. ; software, P.X. ; visualization, P.X. and D.M. ; writing--original draft, P.X. ; writing--review and editing, D.M., A.D.S., X.Z., and X.S. All authors have read and agreed to the published version of the manuscript. This work is supported by the Special Fund of the Institute of Earthquake Forecasting, China Earthquake Administration (Grant No. 2021 IFE0706, 2020 IFE0510, 2021 IFE0708) and funded by the National Key R&D Program of China under Grant No. 2018 YFC1503S05; Pianeta Dinamico-Working Earth (Italian Ministry of University and Research) and Limadou-Science+ (Italian Space Agency) Projects; National Natural Science Foundation of China (Grant number: 41974084); the China Postdoctoral Science Foundation (Grant number: 2021M691190). **Institutional Review Board Statement:** Not applicable.