# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 2 Datasets and Observations
### Dataset and Preprocessing  
All three satellites in the Swarm constellation carry the same scientific payloads to detect ionospheric electromagnetic parameters. This study focused on the use of magnetic field and plasma parameter data from the Swarm A satellite and space weather data. The types of sensors and data utilized in the study are described as follows:  
(1) The vector magnetometer (VFM) is a fluxgate magnetometer from which the on-ground processor provides both high-frequency (50 Hz) and low-frequency (1 Hz) signals.  
Figure 2: Swarm Alpha satellite nighttime track 3 of 7 April 2016 acquired 9 days before the M7.8 Ecuador 2016 earthquake. The description of the subfigures is the same as in Figure 1.  
The magnetic field intensity is available in an instrumental reference system as well as in the Earth one (which is used in this study), consisting of three components, X (North), Y (East), and Z (Vertical), and is measured in nT, while time is measured in Universal Time. The VFM measures field components with an accuracy of 0.1 nT every 3 months for signals at global scales within a space resolution of 20 km [25]. In this study, we focus on the X, Y, and Z components of the low-frequency (1 Hz) data.  
(2) The Langmuir probe (LP) measures the electron temperature (Te), electron density (Ne), and other parameters of plasma by measuring the current generated by electrons and ions at a sampling rate of 1 Hz. This study used plasma Ne data from a level 1b product [24; 25].  
(3) To avoid effects caused by space weather events, we collected the Kp index, an indicator of global geomagnetic activity, to be used as an auxiliary means of discriminating between solar (or geomagnetic activity) and seismic ionospheric disturbance phenomena. It should be noted that data corresponding to periods with a Kp index greater than 3.0 were not analyzed in this study.  
The data used for this study were from Swarm A and included the parameters mentioned above, collected from 1 April 2014 to 30 April 2020, i.e., 7 years of data. According to the United States Geological Survey, 18,621 earthquakes with magnitudes greater than or equal to 4.8 were reported during this period. Thus, we used the same technique as reported by [PERSON] et al. [14] to exclude aftershocks from the list of earthquakes in order to prevent mixing pre- and post-seismic effects. After this process, the final list comprised 9017 independent earthquakes. We also removed data that corresponded to the aftershocks.  
To test the reliability of the machine learning methods and improve their robustness, we created the same number of artificial non-seismic events as actual earthquakes, while randomizing and changing the timings and locations to avoid overlapping with real earthquakes. Within the selected spatio-temporal range, we sampled latitude, longitude, and time at random, adhering to the following constraints: (1) the latitude or longitude is not within 10\({}^{\circ}\) of the latitude or longitude of a real earthquake and (2) the time is not within 15 days of the occurrence time of a real earthquake.