## 5 Discussion  
Many studies have shown the presence of infrared longwave anomalies before earthquakes, but these studies have limitations. Researchers have obtained good anomaly predictions in different case studies using different methods and data, but similar results may not be obtained with a different method, data, or case. [PERSON] et al. [52] used the two-year relative wavelet power spectrum method, and [PERSON] et al. [53] used the wavelet power spectrum method based on FY-2E bright temperature data to study the 14 April 2010 Yushu _M_s7.1 earthquake in China. The spatial distribution and intensity of the anomaly spatiotemporal evolution results obtained by the two differed significantly. [PERSON] et al. [9] found no significant anomalies when they studied the 25 April 2015 M88.1 earthquake in Nepal using the RST algorithm and OLR data. However, [PERSON] et al. [54] obtained good anomaly prediction performance near the epicent when studying the same earthquake case using the power spectrum method and bright temperature data. In addition, traditional methods for studying anomalies in longwave radiation data have been biased towards the representation of thermal anomalies, such as the RST algorithm, where values less than zero are ignored in the calculation of the Alice index [55]. Addressing the two limitations mentioned above, the method of this paper is innovative in two ways. The traditional thermal anomaly extraction model is only a model. In contrast, the BILSTM time series prediction model used is a deep learning model with the ability to train data and learn data, which is a data-driven approach. The training process does not change the original data for earthquake cases and data sources. The model can learn the data trend in the period before the earthquake. In the prediction process, the model parameters are modified to make the predicted value as close to the actual value as possible to achieve a better prediction result. The anomaly detection method used calculates 95% confidence intervals for the OLR, forming several ranges with upper and lower bounds. True values falling within this range are considered to be free of anomalies, while true values above the upper bound or below the lower bound are considered to be anomalous. This approach implies that there are two different types of anomalies. If the anomalyanomaly is below the lower bound, we consider it to be a cold anomaly. While traditional methods can only show anomalies of high longwave radiation values, our method has both hot and cold anomalies, subdividing the types of anomalies that exist before the earthquake [2; 3]. We have tried to explain cold anomalies at a physical level. According to the results of the rock experiments, compression leads to an increase in temperature, and tension leads to a decrease in temperature. The location of the Yangbi earthquake and the results of a multi-period GNSS strain rate field based on GPS data indicate that the Yangbi earthquake is located in a tension zone; the Luding earthquake is in a zone of weakness at the high-value edge of the shear strain of a large strike-slip fault zone and a zone of tensor strain perpendicular to the fault direction. Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) was used to explain the physical and chemical mechanisms of the various anomalies that appear at the surface, in the atmosphere, and in the ionosphere during the gestation and occurrence of earthquakes [56]. It was found that there was a relationship between pre-earthquake OLR and ionospheric anomalies, which can be explained by LAIC [57; 58; 59]. [PERSON] et al. studied strong earthquakes with magnitudes greater than 6 in Taiwan and concluded that seismic OLR anomalies may originate from electromagnetic radiation as well as gas emissions [60]. We tried to compare anomalous the OLR results of the two earthquakes with the anomalous results of TEC obtained by other scholars. [PERSON] et al.