The Seismo-Ionospheric Disturbances before the 9 June 2022 Maerkang Ms6.0 Earthquake Swarm

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 (f(O)F(2)) 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 degrees similar to 40 degrees N, 92 degrees similar to 112 degrees 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.