## 2 AETA System
### Data Set Construction  
AETA raw data contains electromagnetic signals and geoacoustic signals. Most of the anomalies of AETA data occur within \(n\) days before an earthquake, where \(n\) is 27 for electromagnetic signals and \(n\) is 10 for geoacoustic signals. The reason for the value of \(n\) can be confirmed in Section 3. This paper selects the feature data of the first \(n\) days as the input sample and the last 7 days as the output sample. The current time is \(T_{0}\), the first \(n\) days are \(T_{-n}\), and the last 7 days are \(T_{+7}\), earthquake-prediction tasks are defined as follows:  
\[\widetilde{y}_{1}=F_{1}\big{(}x_{T_{-n}},x_{T_{-n+1}},\;\ldots,\;x_{T_{-1}} \big{)},\widetilde{y}_{1}\in[0,1] \tag{1}\]  
\[\widetilde{y}_{2}=F_{2}\big{(}x_{T_{-n}},x_{T_{-n+1}},\;\ldots,\;x_{T_{-1}} \big{)} \tag{2}\]  
\[\widetilde{y}_{2}=\Big{[}\widetilde{y}_{mag},\widetilde{y}_{lat},\widetilde{y }_{lon}\Big{]},\;\widetilde{y}_{mag}\in[3.5,8],\;\widetilde{y}_{lat}\in[22,34], \;\widetilde{y}_{lon}\in[98,107], \tag{3}\]  
where \(\widetilde{y}_{1}\) is the result of earthquake prediction, and \(F_{1}\) is the earthquake prediction model; \(\widetilde{y}_{2}\) is the prediction result of magnitude and epicenter location, and \(F_{2}\) is the corresponding prediction model, \(x\) represents the input feature data, and \(\widetilde{y}_{mag}\), \(\widetilde{y}_{lat}\), \(\widetilde{y}_{lon}\) represent the predicted magnitude, and epicenter latitude and longitude, respectively. In terms of the extraction range of the dataset, a typical earthquake-prone region in China has been selected for earthquake prediction in this paper. To be specific, this region is located in the Sichuan-Yunnan region (\(22.00^{\circ}\) N \(\sim 34.00^{\circ}\) N, \(98.00^{\circ}\) E \(\sim 107.00^{\circ}\) E), which has suffered a total of 206 earthquakes with magnitude larger than 3.5 from January 2017 to January 2021, according to the China Earthquake Network ([[http://www.ceic.ac.cn/history](http://www.ceic.ac.cn/history)]([http://www.ceic.ac.cn/history](http://www.ceic.ac.cn/history)), accessed on 10 March 2021). Figure 1 shows the distribution of stations and earthquakes with magnitude above Ms3.5 in the Sichuan-Yunnan region over 5 years. Moreover, the epicenter distribution shows a typical regional clustering effect. In order to improve the accuracy of prediction, the Sichuan-Yunnan region is divided into three areas according to the clustering feature of earthquake distribution. The three areas are marked out with different color boxes in Figure 1. Area1 is marked out with green box, area2 is marked out with brown box and area3 is marked out with pink box. The range of the stations are expressed in Equations (4)-(7) in order to reduce the effect of random deviation generated by the division of regions and allows the station data in the overlapping regions to be comprehensively trained on both regional models. \[\text{area1}=(30^{\circ}-\Delta\text{N}\;\sim\;34^{\circ}+\Delta\text{N},\;10 2^{\circ}\;-\;\Delta\text{E}\;\sim\;106^{\circ}+\Delta\text{E}) \tag{4}\]  
\[\text{area2}=(26^{\circ}-\Delta\text{N}\;\sim\;30^{\circ}+\Delta\text{N},\;10 2^{\circ}\;-\;\Delta\text{E}\;\sim\;106^{\circ}+\Delta\text{E}) \tag{5}\]  
\[\text{area3}=(24^{\circ}-\Delta\text{N}\;\sim\;28^{\circ}+\Delta\text{N},\;98 ^{\circ}\;-\;\Delta\text{E}\;\sim\;102^{\circ}+\Delta\text{E}) \tag{6}\]  
\[\Delta=1^{\circ} \tag{7}\]Earthquake-prediction models are built in each of the three regions with the sample matrices obtained by sliding windows [33]. The step of the sliding window is set to 1 day, all features are arranged in time sequence by day, and the one-dimensional sequence sample set can be generated along the time dimension according to this sliding window. In order to enhance the robustness of the data, this paper checks the data loss rate for each sliding window matrix, and if the loss rate \(>\) 0.3, this sample is to be discarded. The loss rate is chosen as 0.3, which is an experimental result.