## 3 Methods
### Anomaly Assessment Method  
The confidence level, which belongs to statistics, has also been widely used in the earthquake field. [PERSON] et al. [46] studied the load/unload response ratio (LURR) time series before earthquakes and found that the confidence level of LubRR anomalies was highly correlated with the occurrence of large earthquakes. LubRR precursor anomalies preceded most earthquakes with a probability higher than 90%. [PERSON] et al. [47] used a confidence interval of 95% for anomaly selection in a statistical analysis of radon data from the Wenchuan earthquake in their analysis of the global seismic activity in 12 scenarios by magnitude and period. [PERSON] et al. [48] found a significant activity cycle of about 50 years with a confidence level well above 95%. [PERSON] et al. [49] used the 95% confidence level as an anomaly test in their analysis of aerosol optical depth (AOD) time series data. [PERSON] et al. [50] proposed a new method for pre-seismic TEC detection using the time series method, in which the difference between the predicted and actual values of the time series prediction model was presented. The upper and lower limits were the residual values with a statistical ratio above 95%. The range was obtained by adding and subtracting from the predicted value, and any actual value outside this range was considered an anomaly. [PERSON] et al. [23] also applied this method to detect anomalies in a pre-earthquake longwave radiation time series and successfully detected significant anomalies. We have made improvements to the above methods. We trained several days of data and predicted OLR values for the corresponding number of days. Then, we calculated the residual value of the original data and obtained the 95% confidence interval of the predicted value. When the true value exceeded the upper or lower bound of the confidence interval, the datapoint was determined as an anomaly. Figure 6 shows the detailed anomaly extraction method process.  
Using the Luding earthquake 31.5\({}^{\circ}\) N 102.5\({}^{\circ}\) E grid data as an example, 95% confidence intervals were calculated for the predicted values after the use of the time series to predict the longwave radiation values. The red curve shows the predicted value 30 days before the earthquake, and the blue curve shows the upper 95% confidence interval, the light blue  
Figure 4: LSTM unit structure diagram. \(h_{t-1}\) and \(h_{t}\) are the hidden information of the previous cell output and this cell output, respectively; \(x_{t}\) and \(y_{t}\) are the input and output of this cell, respectively; \(C_{t-1}\) and \(C_{t}\) are the cell states of the previous cell output and this cell output, respectively; \(f_{t}\), \(i_{t}\), and \(o_{t}\) are the objective functions; \(\sigma\) is the sigmoid function.  
Figure 5: BILSTM structure diagram. \(x_{t}\) and \(y_{t}\) are the input and output of the model, respectively.  
curve shows the lower 95% confidence interval, and the green curve shows the true value of the grid. In the graphs, we considered the longwave radiation values to be anomalous if the actual values were below the lower bound of the interval or above the upper bound of the interval. For the 31.5\({}^{\circ}\) N 102.5\({}^{\circ}\) E grid of the Luding earthquake, Figure 7 shows the predicted OLR values and the anomaly performance.