## 3 Analysis Results
### Spatial and Temporal Distribution of TEC Anomalies  
Analyzing the spatiotemporal distribution of anomalies can provide a deeper understanding of the correlation between anomaly changes and the epicenter. It allows for a more comprehensive comprehension of the spatial characteristics of ionospheric disturbances associated with earthquakes. The geostationary nature of geostationary satellites ensures  
Figure 2: The time sequence change of TEC 15 days before the Jüuzhaigou Ms7.0 earthquake. (Relative TEC change curves are shown in blue lines, and upper and lower thresholds calculated by sliding interquartile range are shown in gray lines. The red curve in the bottom of each panel indicates relative change lasting more than 30 min). that each IPP maintains a fixed position, enabling the analysis of the spatiotemporal distribution of abnormal TEC changes. Building upon the previously discussed ionospheric background environment and temporal analysis findings, we proceeded to conduct spatial and temporal analysis of TEC data during periods characterized by relatively tranquil space weather conditions. To facilitate visualization, spatial distribution maps with a temporal resolution of 2 h were generated on a daily basis. The disturbance was extracted according to the sliding interquartile range method mentioned above. For the observation data that exceeded the threshold, the difference between the observation value and the threshold is displayed at the corresponding IPP. Figure 3 shows the spatial and temporal distribution of TEC disturbances on days with significant anomalies, especially 28 July, 29 July, 1 August, 2 August, and the day of the earthquake (8 August). Compared with the temporal analysis, Figure 3 additionally shows the spatial distribution of TEC anomalies relative to the epicenter. On 28 July, negative anomalies, which were mainly concentrated on the south side of the epicenter, occurred in UT04. Positive anomalies were observed in the vicinity of the epicenter at UT08, while regions farther away continued to exhibit negative anomalies. By UT10, the positive anomaly area expanded, and subsequently, the southern side of the epicenter displayed predominantly negative anomalies. On 29 July, notable disturbances were witnessed on the southern side of the epicenter at UT02, characterized by a mixture of positive and negative anomalies. On 1 August at UT16, a significant surge in anomalies took place on the southwest side of the epicenter, primarily indicating positive anomalies. On 2 August at UT12, another noteworthy increase in anomalies occurred on the southern side of the epicenter, with the highest magnitude point located at a certain distance from the epicenter. On the day of the earthquake, 8 August, significant disturbances were observed at UT04, UT06, and UT12, along with an increased number of anomaly points. TEC decreased in the vicinity of the epicenter while it increased in the farther areas. Figure 3: _Cont._  
Attempting to study the spatial characteristics of TEC pre-earthquake disturbances, we further analyzed the time variation of disturbances in terms of their distance from the epicenter. The distance between each IPP and the epicenter was calculated using TEC observations taken every 30 s, which were graphed with the time and distance at a resolution of 150 km. The distribution of IPPs is uneven, which may result in the absence of data within certain distance ranges and affect the analysis results. The analysis revealed that the TEC data existed at each distance, as shown in Figure 4. By setting upper and lower thresholds as M \(\pm\) 1.5 IQR, the anomalous TEC data were highlighted over time and distance (Figure 5). The pictures in the abnormal days mentioned in the former illustration were enlarged and are displayed in Figure 5 for better visualization. Corresponding to the spatiotemporal distribution results, the disturbances appeared to affect an area of nearly 2500 km on 28 July, as shown in Figure 5b.