## 4 Conclusions  
Based on the relative power spectrum method, we propose a statistical method based on connected domain recognition to calculate the PPVs, TPRs and probability gains in \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Authors** & **Data** & **Region** & **Period** & **Magnitude** & **PPV** & **TPR** & **Gain** & **Anomaly** \\ \hline \begin{tabular}{c} [PERSON] et al. [46] \\ \end{tabular} & OLR 1 & Taiwan, & 2009–2019 & \(\geq\)6.0 & None 5  & None & \(<\)25 days \\ \begin{tabular}{c} [PERSON] et al. [28] \\ \end{tabular} & ST 2 & \begin{tabular}{c} Sichuan, \\ China \\ \end{tabular} & 2002–2018 & \(\geq\)5.0 & 25.9\% & 10.8\% & \(<\)1.5 & \(<\)30 days \\ \begin{tabular}{c} [PERSON] et al. [31] \\ \end{tabular} & OLR & China & 2007–2017 & \(\geq\)4.0 & 76.1\% & 58.1\% & 1.34 & \(<\)50 days \\ \
\begin{tabular}{c} [PERSON] et al. [23] \\ \end{tabular} & TIR 3 & Turkey & 2004–2015 & \(\geq\)4.0 & 32.9\% & \(\approx\)12\% 6 & 1.3 & \(<\)30 days \\
\begin{tabular}{c} [PERSON] et al. [29] \\ [PERSON] et al. \\ \end{tabular} & ST & Global & 2010–2018 & \(\geq\)5.0 & None & \(\approx\)20\% & 2.2 & \(<\)30 days \\ \hline \hline \end{tabular}  
\end{table} 
Table 7: The statistical results in previous studies. Figure 21: Molchan diagram of different methods. Figure 20: The number of days between anomalies and earthquakes. different channels. The results show that the PPV and TPR could be improved by increasing the distance threshold. The probability gain is low and its change with distance threshold is not obvious. In addition, we also statistically analyzed the multi-channel infrared anomalies before 358 earthquakes. There is at least one channel anomaly within one kilometer of the epicenter within one month before 36.87% of the earthquakes, but there are only 26 earthquakes with four-channel anomalies within 400 km of the epicenter and 18 earthquakes with four-channel anomalies within 200 km of the epicenter. In the study of three earthquake cases, four-channel anomalies appeared and disappeared before the earthquake. The epicenter is at or some distance from the edge of the anomaly. Due to the low PPV and probability gain of the earthquake prediction method using single-channel data, multi-channel infrared remote sensing images are used for earthquake prediction. The PPV of four-channel anomalies is 41.94%. This is higher than that of single-channel anomalies at the same distance threshold of 200 km. Meanwhile, the method causes a lower TPR. Significantly, the spatial-temporal occupancy of four-channel anomalies is very low, and the probability gain is doubled. This study shows the difference between pre-earthquake anomalies in multi-channel infrared remote sensing images and indicates that multi-channel infrared remote sensing images may have more advantages in the PPV and the probability gain of earthquake prediction than single-channel data. In earthquake prediction, the PPV, which indicates the reliability of the algorithm, is more important than the TPR. This is because the accurate prediction of a single earthquake can also save a lot of life and property. However, it is still difficult to use the four-channel infrared data to obtain high enough accuracy for the practical application of earthquake prediction. The purpose of this paper is to show the advantages of multi-channel data over single-channel data. The results could be compared with other types of pre-seismic anomalies to study the mechanism of anomalies during the earthquake preparation period and explore a better method for earthquake prediction. Earthquake prediction needs a lot of remote sensing data and ground-based observation data. In future studies, we can improve the performance of earthquake prediction with the following aspects:  
1. FY-2 contains eight satellites. By assimilating data from multiple satellites, it is possible to capture longer observations and count more earthquakes;
2.