# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 1 Introduction  
The ionosphere is an important layer of the solar-terrestrial space observation environment, and the process of earthquake preparation and occurrence can also cause anomalous changes in ionospheric parameters over the preparation zone, known as seismic ionospheric disturbances. These phenomena are a manifestation of earthquakes in the ionosphere, a result of lithosphere-atmosphere-ionosphere coupling, and are considered to be one of the more promising ideas for detecting short-term earthquake signals. Decades ago, [PERSON] [1] and [PERSON] [2] first reported anomalous ionospheric perturbations associated with the 1964 Alaska earthquake in the USA, and studies on seismic ionospheric phenomena have been rapidly developed. With the accumulation of available observational resources, seismic ionospheric phenomena have been detected, reported, and confirmed [3]. Currently, these phenomena are an issue of great concern and have become a hot topic atthe intersection of seismology and space physics. In the 21 st century, with the development of space satellite observation technology, many countries launched satellites dedicated to monitoring space environment changes and natural disaster activities such as earthquakes and volcanoes, for example the QuakeSat (USA), SICH-1M (Ukraine), COMPASS-2 (Russia), DEMETER (France), and China Seismo Electromagnetic Satellite (CSES). Furthermore, even satellites dedicated to other scientific purposes have been demonstrated to provide important observations for seismic ionospheric disturbances, such as the Swarm constellation of the European Space Agency (ESA). The Swarm constellation consists of three identical satellites that carry the same payloads. The combined observation of multiple satellites in the Swarm constellation offers significant advantages over single-satellite observations, allowing for better detection efficiency, better global coverage in a day, higher spatial resolution, and improved capabilities for analyzing anomalies. In this way, Swarm represents a new, successful approach to the study of seismic ionospheric phenomena. Studies of these phenomena from the Swarm satellites are currently ongoing, yielding continuous investigation and reporting of the valuable description of their mechanisms. A review of previous studies on seismic ionospheric phenomena based on Swarm satellites shows that the work is generally divided into two types: earthquake case studies and statistical investigations, both of which focus on the analysis of ionospheric disturbance phenomena associated with the preparation phase of an earthquake before its occurrence. Both types explore the precursor and provide related criteria potentially useful for earthquake prediction and forecasting. Various perturbations have been observed using Swarm data before the occurrence of large earthquakes. [PERSON] et al. [4] investigated magnetic field anomalies from Swarm data for one month before and after the magnitude 7.8 Nepal earthquake that occurred on 25 April 2015 at 06:26 UTC, and found that the cumulative number of anomalies followed the same typical power-law behavior of a critical system close to its critical time, thereafter returning to normal after the earthquake. In another study, [PERSON] et al. [5] used multi-parameters from ground and space, that is, Earth geomagnetic field data (magnetic data from the ESA Swarm constellation and from L'Aquila and Duronia ground observatories of the INGV (Italian National Institute of Geophysics and Vulcanology)) along with the MERRA-2 climatological dataset to study the lithosphere-atmosphere-ionosphere coupling effects of the 2016-2017 central Italy earthquake sequence. They revealed anomalies in the ground-based geomagnetic observations 275 and 85 days before the earthquake sequence, anomalies from satellite observations 240 days and 3 days before the start of the earthquake sequence, and two perturbations in the chemical/physical composition of the atmosphere 200 and 150 days prior to the earthquake sequence. In a further study, [PERSON] et al.