# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 5 Discussions  
Several studies have investigated the physical mechanisms of ionospheric pre-earthquake perturbations [47; 48; 49; 50; 51]. Perhaps the most widely accepted hypotheses regarding these mechanisms were presented by [PERSON], et al. [52] and [PERSON], [PERSON], and [PERSON] [27], who proposed complex lithosphere-atmosphere-ionosphere coupling as the physical basis for the generation of short-term earthquake precursors. Specifically, the lower crust and upper mantle generate gases such as radon (Rn) at lithostatic pressure during the buildup to an earthquake, and this gas could form large-scale domains in the rock. When reaching a certain vertical extent, these gas domains could become hydrostatically unstable and force their way upward through the lithosphere. Rapid lithospheric degassing can be expected to trigger several atmospheric processes near the Earth's surface, leading to  
Figure 12: Ternary probability diagrams illustrating the findings of SafeNet on the test data for (**A**) class 0, (**B**) class 1, and (**C**) class 2. Class 0 is represented by blue crosses, class 1 by red crosses, and class 2 by green crosses; the color of the cross indicates the actual class, and the distance projected from each cross to the class axis represents the probability of that class in the model prediction. (**D**) Matrix of confusion illustrating the distribution of estimated and actual values. Each tile’s center contains the normalized count (overall percentage) in black text. Column percentages are shown at the bottom of each tile, while row percentages are displayed on the right (both in red text). The sum tiles on the plot’s right and bottom (in shades of green) indicate the overall distribution of predictions and targets. Note that the color intensity is proportional to the number of counts. changes in air conductivity and, therefore, changes to the near-ground atmospheric electric field. It is known that, as a part of the global electric circuit, the ionosphere immediately reacts to changes in near-ground electric properties, and an electric field induced within the ionosphere can cause ion drift and irregularities in electron concentrations. To better understand the mechanisms of ionospheric pre-earthquake perturbations, [PERSON] [53] performed a series of experiments on a loaded rock and found that stress activation of p-hole charge carriers in the Earth's crust led to regional positive ground potential. In these scenarios, ionospheric perturbations are expected. Crustal strain measurements often fail to detect any unusual changes before earthquakes [54], and the measurement of Rn gas content is among the most reported earthquake precursors [55,56]. Moreover, continuous monitoring of soil gas radon and water radon concentrations along with the Amritsar (Punjab, India) seismic zone correlation showed that the amplitude of radon gas anomalies was positively correlated with earthquake magnitude [57]. In addition, recent studies revealed a significant decrease in radon concentration within continuous measurements of radon concentration in the atmosphere before the 2018 earthquake in northern Osaka, Japan [58] and peculiar changes in radon concentration in the atmosphere two months before the 1995 Kobe earthquake in Japan [59]; [PERSON] et al. [60] studied the radon gas anomalies in northern and northeastern Taiwan before the earthquake and observed a significant increase in soil radon concentration from a few days to a few weeks before the earthquake. Finally, it is reasonable that the amount of emitted radon could depend on the rupture length of the fault, that is, the magnitude of the seismic event. This study considers Rn gas emissions as the most reasonable result to be initially triggered by an earthquake.