# Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES
## 3 Results
### Summative Analysis  
The summative analysis was conducted based on the results of each EQ case analyzed in the same manner as in Section 3.1. In this work, all 47 shallow EQs with M \(\geq\) 7 and depth \(\leq\) 100 km that occurred from February 2018 to February 2023 were analyzed. It is noted that not all payloads can completely collect observations during all 47 EQs. One reason is that most payloads turn off over latitudes of \(\pm 65^{\circ}\)[21], so EQs that occurred higher than this latitude will be missed for recording. Only HPM, which operates over the whole latitude range, has the opportunity to collect observations for all 47 EQs. Another reason is that the payload might malfunction or be under experiment coincidentally during the EQ. For example, the recordings of EFD around the equatorial region interfered with the artificial signals from the satellite platform; thus, the recordings from EFD were not qualified for EQs that occurred in this region [22]. As a result, the number of EQs analyzed based on EFD is less than 47. The third reason is that for some payloads, such as GOR, data acquisition depends on the location of the occultation events, so it has less chance to recode EQs. After careful data selection and examination, we finally collected reliable data for 38 EQ cases from payloads including SCM, LAP, PAP, and HEPP, for 32 EQ cases from GOR and EFD, and for 47 EQs from HPM, as summarized in Table 2 and Figure 8. Table 2 highlights the number of EQs showing certain ionospheric anomalies recorded by at least one payload. Figure 7: The comparison maps of integrated energetic electron flux within the 0.1–0.3 MeV during the nightside, centered around T\(\ddot{\text{u}}\)rkiye EQ. The stars denote the epicenters, while the arrows highlight the anomalies. (**a–f**) correspond to periods of 11–15 January, 16–20 January, 21–25 January, 26–30 January, 31 January–4 February, and 5–9 February, respectively. The red arrows indicate anomalies that may be related to earthquakes. Based on the results presented in Table 2, the anomaly recognition rates for these analyzed global shallow strong EQs for each payload have been calculated and are depicted in Figure 8. Here, the anomaly recognition rate for each payload is defined as the ratio of the number of EQs with ionospheric anomalies observed to the number of EQs for which that payload has reliable observations. Take the example of SCM: a total of 38 EQs were analyzed, and SCM observations during 27 EQs showed potential seismic-related phenomena; thus, the anomaly recognition rate for SCM is 71% (27/38). It is evident that LAP, EFD, and SCM exhibit anomaly recognition rates for global shallow strong EQs exceeding 70%, indicating their good detection capability during these seismic events. HEPP, HPM, and GOR register approximately 50% anomaly recognition rates. PAP has a low anomaly recognition rate of 24% due to less qualified data after it became contaminated. It is noteworthy that, despite the detection rate of HPM not being the highest, the anomalies identified by HPM have been deemed more reliable than those from other payloads, according to a predictive experiment we carried out by using space-ground comparative observations in southwest China (unpublished). Additionally, for each EQ case, we assessed the number of payloads that simultaneously captured the anomalous phenomena, followed by a summative analysis encompassing all 47 strong shallow earthquakes, as shown in Figure 9, where the horizontal axis represents the groups of payloads (set as 1, 2, 3, 4, 5+ payloads) that simultaneously recorded anomalies for the same EQ event, and the vertical axis represents the number