# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 1 Introduction
[6] analyzed the Swarm satellite magnetic data prior to the magnitude 7.1 California Ridgecrest earthquake that occurred on 6 July 2019 and found some increase in anomalies in the Y (eastern) component of the magnetic field around 200 days before the earthquake; moreover, 15 min before the earthquake, the Swarm Bravo satellite passed right over the epicentral region, and its Y component presented interesting anomalies. [PERSON] et al. [7] analyzed ionospheric magnetic field data from the Swarm Alpha satellite before the 16 April 2016 Ecuador earthquake (magnitude 7.8) based on the non-negative matrix factorization method and found that the energy and entropy of one of the weighting components were more concentrated within the preparation region of the seismic event. In that study, the cumulative number of orbits with anomalies inside that region showed an acceleration before the Ecuador earthquake and recovered to a linear (i.e., standard) trend after the earthquake. In addition to the aforementioned examples, numerous studies have confirmed that the Swarm ionospheric perturbations are sensitive enough and useful for detecting earthquake-related anomalies, such as for the 2014 Ludian earthquake [8], 2017 Sarpol-e Zahab (Iran) earthquake [9], 2017 Mexico earthquake [10], and the above mentioned 2016 Ecuador earthquake [11; 12]. Statistical analysis is a common technique for investigating ionospheric anomalies that occur before earthquakes using satellite data [13; 14; 15]. [PERSON] et al. [16] statistically analyzed the Swarm electron density and magnetic field data observed by the three Swarm satellites over 4.7 years using a superposed epoch approach and found that some electron density and magnetic anomalies were significantly concentrated from more than two months before the earthquake to a few days prior to the earthquake. This confirmed the well-known Rikitake empirical law between the time of the precursors and the magnitude of the earthquake by studying different magnitude ranges. In another statistical analysis of 5.3 years of magnetic field data from the Swarm satellite by [PERSON] et al. (2017), the distance from the satellite to the earthquake epicenter matched the measured distance arrival time of the coseismic disturbance from the surface to the ionosphere, confirming that observed anomalies were likely to be caused by seismic events due to their occurrence with a mixed transmission mechanism, i.e., by acoustic gravity waves and electromagnetic propagation in the ionosphere. However, most existing pre-earthquake anomaly studies are lacking in consistent analysis methods and anomaly evaluation metrics; thus, the analysis results of anomalies lack universality and often lead to various or even contradictory interpretations for the same earthquake. Moreover, statistical studies of seismic anomalies often do not consider the influence of non-seismic anomalies. Deep learning, which has been widely used in recent earthquake research ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018), could perform consistent analysis and assessment of a large number of earthquakes, and could take into account the influence of non-seismic anomalies, which are an effective tool to solve the above issue. By investigating different DEMETER satellite datasets, [PERSON] et al. (2018) confirmed some frequency bands with low-frequency electric and magnetic fields to be the main features for pre-seismic electromagnetic perturbation identification using deep learning. [PERSON] et al. (2018) also proposed a deep learning framework termed SeqNetQuake by training whole life cycle dataset from the DEMETER satellites and transferring the well-trained model to the CSES satellite to form a new identification model which achieved a 12% improvement in classification performance. Based on the classical AdaBoost machine learning algorithm and the feature of satellite remote sensing products such as infrared and hyperspectral gases, [PERSON] et al.