In this paper, we focused on the characteristics of the seismo-ionospheric effects related to two successive earthquakes, namely, the earthquakes in 2022 in Taitung Sea, Taiwan, China, with magnitudes (M) of 6.7 and 6.3, at 23.45° N, 121.55° E and 23.39° N, 121.52° E and with the same focal depth of 20 km, which were detected by the China Seismo-Electromagnetic Satellite (CSES). By applying the sliding interquartile range method to electron density (Ne) data acquired by the Langmuir probe (LAP) onboard the CSES and the grid total electron content (TEC) data obtained from the Center for Orbit Determination in Europe (CODE), positive anomalies were found under quiet geomagnetic conditions on 2‒3 March and 8‒9 March 2022—that is, 19–20 and 13–14 d before the earthquakes, respectively, and the global ionospheric mapping (GIM) TEC data suggested that anomalies may also have been triggered in the magnetic conjugate area 13–14 d prior to the earthquakes occurrences. In addition, the CSES Ne data showed enhancements 3 and 5 d before the earthquakes occurred. Furthermore, 138 earthquakes with M ≥ 5.0 that occurred in Taiwan and the surrounding region during the period February 2019 to March 2022 were statistically analyzed using the CSES Ne data. The results show that most of the Ne anomalies were positive. Moreover, the greater the earthquake magnitude, the greater the frequency of the anomalies; however, the amplitude of the anomalies did not increase with the earthquake magnitude. The anomalies were concentrated during the period of 10 d before to 5 d after the earthquakes. No increase in the amplitude of anomalies was observed as the time of the earthquakes approached. Finally, based on evidence relating to earthquake precursor anomalies, we conclude that it is possible that earthquakes in Taiwan and the surrounding region affect the ionosphere through the geochemical, acoustic, and electromagnetic channels, as described by the lithosphere‒atmosphere‒ionosphere coupling (LAIC) model, and that the two studied earthquakes in Taiwan may have induced ionospheric effects through the geochemical channel.

Based on the multi-data of the global ionospheric map (GIM), ionospheric total electron content (TEC) inversed from GPS observations, the critical frequency of the F2 layer (fOF2) from the ionosonde, electron density (Ne), electron temperature (Te), and He+ and O+ densities detected by the China Seismo-Electromagnetic Satellite (CSES), the temporal and spatial characteristics of ionospheric multi-parameter perturbations were analyzed around the Maerkang Ms6.0 earthquake swarm on 9 June 2022. The results showed that the seismo-ionospheric disturbances were observed during 2–4 June around the epicenter under quiet solar-geomagnetic conditions. All parameters we studied were characterized by synchronous changes and negative anomalies, with a better consistency between ionospheric ground-based and satellite observations. The negative ionospheric anomalies for all parameters appeared 5–7 days before the Maerkang Ms6.0 earthquake swarm can be considered as significant signals of upcoming main shock. The seismo-ionospheric coupling mechanism may be a combination of two coupling channels: an overlapped DC electric field and an acoustic gravity wave, as described by the lithosphere–atmosphere–ionosphere coupling (LAIC). In addition, in order to make the investigations still more convincing, we completed a statistical analysis for the ionospheric anomalies of earthquakes over Ms6.0 in the study area (20°~40° N, 92°~112° E) from 1 January 2019 to 1 July 2022. The nine seismic events reveal that most strong earthquakes are preceded by obvious synchronous anomalies from ground-based and satellite ionospheric observations. The anomalous disturbances generally appear 1–15 days before the earthquakes, and the continuity and reliability of ground-based ionospheric anomaly detection are relatively high. Based on the integrated ionospheric satellite–ground observations, a cross-validation analysis can effectively improve the confidence level of anomaly identification and reduce the frequency of false anomalies.