# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 4 Results  
We used the Swarm dataset to train the proposed SafeNet model directly, which had been split into the training and test sets. Initially, we configured the data with 60 consecutive observations as the input sequence length, a spatial window centered at the epicenter, a deviation of the Dobrovolsky radius [29], and nighttime data in the initial configuration, because there is no universal standard for lengths of the input sequence and the spatial window (DataSet 01 in Table 1). In our study, we consistently considered data from 15 days before to 5 days after every earthquake and set it as the temporal window.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DataSet** & **Night/Daytime** & **Spatial Feature** & **Input Sequence** & \
\begin{tabular}{c} **Earthquake Magnitude/No.** \\ **of Real Earthquakes/Positive** \\ **to Negative Ratio** \\ \end{tabular} \\ \hline DataSet 01 & Nighttime &
\begin{tabular}{c} with its center at the epicenter \\ and the Dobrovolsky radius \\ with its center at the epicenter \\ and the Dobrovolsky radius \\ with its center at the epicenter \\ and the Dobrovolsky radius \\ with its center at the epicenter \\ and the Dobrovolsky radius \\ with its center at the epicenter \\ and the Dobrovolsky radius \\ with its center at the epicenter \\ and the Dobrovolsky radius \\ with its center at the epicenter \\ and the Dobrovolsky radius \\ with its center at the epicenter \\ and a deviation of 3\({}^{\circ}\) \\ with its center at the epicenter \\ and a deviation of 7\({}^{\circ}\) \\ with its center at the epicenter \\ and  
As illustrated in Figure 5, the ROC curves were used as a performance measure because they represent relative trade-offs between true positives (benefits) and false positives (costs) for each class, and the performance of the model utilizing nighttime data is shown. The AUC values of classes 0 and 1 were both greater than 0.9 (Figure 5A,B), indicating that the model can roughly distinguish time series related to earthquakes and non-seismic events, but the AUC of class 2 was only 0.50 (Figure 5C), indicating that the accuracy of the model in identifying space weather such as magnetic storms was low. This may be due to the fact that class 2 was trained with a lower number of samples, causing the model to fail to extract the features of the class. Figure 5D depicts the MCC, F1 score, and accuracy bar plot curves, which represent the overall performance of the model. The findings were similar to those implied by the ROC curves, showing that the model could distinguish earthquakes from non-seismic and space events to some degree. In general, the performance of the model based on the initial setup was reasonable, but not remarkable. As a result, we investigated whether combining datasets with various temporal and geographic characteristics, as well as alternative models, might provide an improved performance.  
Figure 5: Receiver operating characteristic (ROC) curves showing SafeNet’s performance for (**A**) class 0, (**B**) class 1, and (**C**) class 2 utilizing nighttime data. (**D**) Matthews correlation coefficient (MCC), F1 score, and accuracy bar plot curves illustrating the model’s performance with nighttime data.