## 3 Analysis Results
### TEC Time Series Analysis of Stations near the Epicenter of the Earthquake  
There exists a network of 170 monitoring stations equipped to receive GEO satellite data. Each of these stations is specifically associated with five GEO satellites, enabling the acquisition of TEC data pertaining to five IPPs. Utilizing these TEC measurements, it becomes feasible to conduct a distance-related analysis of the ionospheric TEC anomalies by calculating the distance from the epicenter to each IPP. The interquartile range (IQR) is a parameter used to represent the dispersion of data and can be used to check for abnormalities in the data [48]. The sliding interquartile range method is often used to detect anomalies in ionospheric TEC before earthquakes [49; 50]. This method can obtain a more accurate background value by calculating the median value over a period of time and determining the upper and lower limits of the ionospheric TEC based on the IQR, thereby extracting anomalies that exceed the threshold. In this study, six IPPs with more-complete data and nearest to the epicenter location are screened out. They include two IPPs of gspl, two IPPs of qhmq, and one IPP each of dlha and qhgc. In order to extract the perturbation related to the background, the sliding interquartile range method is employed to analyze the time series of TEC at each IPP, and the detailed processes are as follows. 1. The median (M) and IQR of 15 days before each observation were calculated based on the preceding background. Statistically, the expected value of the interquartile range is 1.34 times the standard deviation [50; 51], so disturbances were extracted using the threshold of M \(\pm\) 1.5 IQR. 2. The calculation of abnormal values (Ab) was carried out using Equation (8), which can highlight the anomalies. Values exceeding M + 1.5 IQR were considered as positive anomalies, while values below M \(-\)1.5 IQR were negative anomalies. For values exceeding the threshold, we calculated the relative change (Ab%) between the observed data (Obs) and the threshold. We assigned the value of 0 to those that do not exceed the threshold value. \[Ab\%= \begin{cases}\frac{Obs-(M+1.5 IQR)}{(M+1.5 IQR)}\times 100&Obs>M+1.5 IQR \\ 0&M-1.5 IQR\leq Obs\leq M+1.5 IQR\\ \frac{Obs-(M-1.5 IQR)}{(M-1.5 IQR)}\times 100&Obs<M-1.5 IQR\end{cases} \] (8)  
Figure 1: Time series of F10.7, Dst, and Kp indices from 24 July to 8 August 2017. (In panel of Dst, the red line represents Dst absolute values exceeding 30 nT; in the panel of Kp, the red bar denotes indices greater than 3). 3. To eliminate the interference of abrupt jumps and ensure that anomalies have a certain duration, disturbances with a non-zero Ab% value persisting for over 30 min were highlighted. In addition, the relative change amount is used in Equation (8) because there is daily variation in the TEC data. The TEC value is larger at noon so the disturbance may be easier to observe, while the changes at night are relatively small. Therefore, using relative changes avoids exaggerating perturbations in strong backgrounds and does not ignore small perturbations in weak backgrounds. This allows for more accurate analysis and detection of abnormal changes before earthquakes. Figure 2 depicts the temporal variation of TEC in the 15 days leading up to the earthquake at the six IPs which are closest to the epicenter and have relatively complete data. Each subgraph features the actual observed values, represented by the blue line; the upper and lower thresholds (M \(\pm\) 1.5 IQR), indicated by the gray lines; and anomalies with non-zero Ab% values persisting for over 30 min, shown by the red line. Based on the spatial weather background, it can be inferred that the anomalies observed from 3 August to 6 August are more likely attributed to geomagnetic storm activity. Notably, significant anomalies in terms of relative TEC temporal variations were also detected on 28 July in the relatively calm spatial weather conditions.