The study of the preparation phase of large earthquakes is essential to understand the physical processes involved, and potentially useful also to develop a future reliable short-term warning system. Here we analyse electron density and magnetic field data measured by Swarm three-satellite constellation for 4.7 years, to look for possible in-situ ionospheric precursors of large earthquakes to study the interactions between the lithosphere and the above atmosphere and ionosphere, in what is called the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC). We define these anomalies statistically in the whole space-time interval of interest and use a Worldwide Statistical Correlation (WSC) analysis through a superposed epoch approach to study the possible relation with the earthquakes. We find some clear concentrations of electron density and magnetic anomalies from more than two months to some days before the earthquake occurrences. Such anomaly clustering is, in general, statistically significant with respect to homogeneous random simulations, supporting a LAIC during the preparation phase of earthquakes. By investigating different earthquake magnitude ranges, not only do we confirm the well-known Rikitake empirical law between ionospheric anomaly precursor time and earthquake magnitude, but we also give more reliability to the seismic source origin for many of the identified anomalies.

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The geomagnetic storm that occurred on 25 August 25 2018, that is, during the minimum of solar cycle 24, is currently the strongest ever probed by the first China Seismo‐Electromagnetic Satellite (CSES‐01). By integrating the in situ measurements provided by CSES‐01 (orbiting at altitude of 507 km) and by Swarm A satellite (orbiting at ca., 460 km) with ground‐based observations (ionosondes, magnetometers, and Global Navigation Satellite System receivers), we investigate the ionospheric response at lower‐ and mid‐latitudes over Brazil. Specifically, we investigate the electrodynamic disturbances driven by solar wind changes, by focusing on the disturbances driving modifications of the equatorial electrojet (EEJ). Our proposed multisensor technique analysis mainly highlights the variations in the topside and bottomside ionosphere, and the interplay between prompt penetrating electric fields and disturbance dynamo electric fields resulting in EEJ variations. Thanks to this approach and leveraging on the newly available CSES‐01 data, we complement and extend what recently investigated in the Western South American sector, by highlighting the significant longitudinal differences, which mainly come from the occurrence of a daytime counter‐EEJ during both 25 and 26 August at Braziliian longitudes and during part of 26 August only in the Peruvian sector. In addition, the increased thermospheric circulation driven by the storm has an impact on the EEJ during the recovery phase of the storm. The observations at the CSES‐01/Swarm altitudes integrated with the ground‐based observation recorded signatures of equatorial ionospheric anomaly crests formation and modification during daytime coupled with the positive ionospheric storm effects at midlatitude.

Nowadays, the possibility that medium-large earthquakes could produce some electromagnetic ionospheric disturbances during their preparatory phase is controversial in the scientific community. Some previous works using satellite data from DEMETER, Swarm and, recently, CSES provided several pieces of evidence supporting the existence of such precursory phenomena in terms of single case studies and statical analyses. In this work, we applied a Worldwide Statistical Correlation approach to M5.5+ shallow earthquakes using the first 8 years of Swarm(i.e., from November 2013 to November 2021) magnetic field and electron density signals in order to improve the significance of previous statistical studies and provide some new results on how earthquake features could influence ionospheric electromagnetic disturbances. We implemented new methodologies based on the hypothesis that the anticipation time of anomalies of larger earthquakes is usually longer than that of anomalies of smaller magnitude. We also considered the signal’s frequency to introduce a new identification criterion for the anomalies. We find that taking into account the frequency can improve the statistical significance (up to 25% for magnetic data and up to 100% for electron density). Furthermore, we noted that the frequency of the Swarm magnetic field signal of possible precursor anomalies seems to slightly increase as the earthquake is approaching. Finally, we checked a possible relationship between the frequency of the detected anomalies and earthquake features. The earthquake focal mechanism seems to have a low or null influence on the frequency of the detected anomalies, while the epicenter location appears to play an important role. In fact, land earthquakes are more likely to be preceded by slower (lower frequency) magnetic field signals, whereas sea seismic events show a higher probability of being preceded by faster (higher frequency) magnetic field signals.

We analyse Swarm satellite magnetic field and electron density data one month before and one month after 12 strong earthquakes that have occurred in the first 2.5 years of Swarm satellite mission lifetime in the Mediterranean region (magnitude M6.1+) or in the rest of the world (M6.7+). The search for anomalies was limited to the area centred at each earthquake epicentre and bounded by a circle that scales with magnitude according to the Dobrovolsky’s radius. We define the magnetic and electron density anomalies statistically in terms of specific thresholds with respect to the same statistical quantity along the whole residual satellite track (|geomagnetic latitude| ≤ 50°, quiet geomagnetic conditions). Once normalized by the analysed satellite tracks, the anomalies associated to all earthquakes resemble a linear dependence with earthquake magnitude, so supporting the statistical correlation with earthquakes and excluding a relationship by chance.