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.

On 20 December 2021, after six quiet years, the Hunga Tonga–Hunga Ha’apai volcano erupted abruptly. Then, on 15 January 2022, the largest eruption produced a plume well registered from satellites and destroyed the volcanic cone previously formed in 2015, connecting the two islands. We applied a multi-parametric and multi-layer study to investigate all the possible pre-eruption signals and effects of this volcanic activity in the lithosphere, atmosphere, and ionosphere. We focused our attention on: (a) seismological features considering the eruption in terms of an earthquake with equivalent energy released in the lithosphere; (b) atmospheric parameters, such as skin and air temperature, outgoing longwave radiation (OLR), cloud cover, relative humidity from climatological datasets; (c) varying magnetic field and electron density observed by ground magnetometers and satellites, even if the event was in the recovery phase of an intense geomagnetic storm. We found different precursors of this unique event in the lithosphere, as well as the effects due to the propagation of acoustic gravity and pressure waves and magnetic and electromagnetic coupling in the form of signals detected by ground stations and satellite data. All these parameters and their detailed investigation confirm the lithosphere–atmosphere–ionosphere coupling (LAIC) models introduced for natural hazards such as volcano eruptions and earthquakes.

The coupling processes among the lithosphere, atmosphere, and ionosphere (LAI) during the earthquake preparation phase are still an open scientific debate. Comprehensive LAI coupling effects around the 2022 Ms6.8 Luding earthquake in China are investigated with a multi-parameter and multi-layer approach, including the b-value, revised accelerated moment release, Earth resistivity, ELF magnetic field emissions, atmospheric electric field, surface temperature, foF2 from ionosonde, GNSS TEC, Ne and magnetic field from CSES and Swarm satellites, and energetic electrons from CSES and NOAA satellites. It is found that the anomalies start from the lithospheric parameters as Earth resistivity and b-values 1–2 years before to reflect the local stress loading in the seismic region, then the ionospheric and atmospheric disturbances occur and accelerate −50 days before and −15 days before, and finally the electrons precipitate a few days before. The simultaneous perturbations in LAI illustrate the thermodynamic coupling channel, such as on 24 August, −12 days before. Meanwhile, the abundant developed ionospheric anomalies without atmospheric disturbances demonstrate the electromagnetic coupling way from the lithosphere to the ionosphere directly. Finally, the results demonstrate a two-way model of LAIC: one way is characterized by a slow chain of processes, of thermodynamic nature, starting from the ground and proceeding to the above atmosphere and ionosphere, showing an exponential trend in the cumulative number of anomalies; the second way is characterized by oscillating electromagnetic coupling between the lithosphere and ionosphere, showing intermittent fluctuations in the corresponding cumulative number of anomalies.

On 6 February 2023, Turkey experienced its most powerful earthquake in over 80 years, with a moment magnitude (Mw) of 7.7. This was then followed by a second earthquake of Mw 7.6 just nine hours later. According to the lithosphere–atmosphere–ionosphere coupling (LAIC) models, such a significant seismic activity is expected to cause anomalies across various observables, from the Earth’s surface to the ionosphere. This multidisciplinary study investigates the preparatory phase of these two major earthquakes by identifying potential precursors across the lithosphere, atmosphere, and ionosphere. Our comprehensive analysis successfully identified and collected various anomalies, revealing that their cumulative occurrence follows an accelerating trend, either exponential or power-law. Most anomalies appeared to progress from the lithosphere upward through the atmosphere to the ionosphere, suggesting a sequential chain of processes across these geospheres. Notably, some anomalies deviated from this overall trend, manifesting as oscillating variations. We propose that these anomalies support a two-way coupling model preceding major earthquakes, highlighting the potential role of fluid chemistry in facilitating these processes.

Each Italian earthquake included in the Italian Parametric Catalogue (CPTI) is based on a single study, with its database stored in the Italian Macroseismic Database (DBMI). The DBMI contains macroseismic-intensity data for approximately 5000 Italian earthquakes. However, for the same events, numerous studies have been independently carried out over the years, with the data of such studies not having been incorporated into the DBMI. By consolidating all available data for each event, it is possible to significantly enhance the dataset used for hazard assessments and the reconstruction of local seismic histories. This approach would make studies of individual events much more robust and comprehensive. The objective of this work is to propose the integration of different macroseismic datasets for individual events by identifying criteria that can effectively merge a large number of intensity data points. A total of 45 Italian earthquakes with data from multiple sources were identified and reassessed through a rapid review process. This effort has resulted in the creation of a new dataset (https://doi.org/10.13127/macroseismic/teral024, Tertulliani et al., 2024), substantially increasing the number of macroseismic data points (MDPs) for the earthquakes covered by this study compared to in the DBMI15 (from 2892 to 9328 MDPs). Consequently, the macroseismic distributions for these 45 events have become more detailed, robust, and extensive.

Earthquake prediction has always been a challenging task, and some researchers have proposed that it is an even impossible goal, concluding earthquakes are unpredictable events. Such a conclusion seems too extreme and in contrast with several pieces of evidence of alterations recorded by several instrumentations from the ground, atmosphere, and more recently by Earth Observation satellite. On the other side, it is clear that searching the “perfect precursor parameter” doesn’t seem to be a good way, since the earthquake process is a complex phenomenon. In fact, a precursor that works for one earthquake does not necessarily work for the next one, even on the same fault. In some cases, another problem for precursors identification is the recurrency time between the earthquakes, which could be very long and, in such cases, we don’t have comparable observations of earthquakes generated by the same fault system.In past years, we concentrated mainly on two aspects: statistical and single case study; the first one consists of some statistical evidence on ionospheric disturbances possibly related to M5.5+ earthquakes (e.g., presented at EGU2018-9468, and published by De Santis et al., Scientific Report, 2019), furthermore, some clear signals in the atmosphere statistically preceded the occurrence of M8+ events (e.g., presented at EGU2020-19809). On the other side, we also investigated about 20 earthquakes that occurred in the last ten years, some of them by a very detailed and multiparametric investigation, like the M7.5 Indonesia earthquake (presented at EGU2019-8077 and published by Marchetti et al., JAES, 2020), or the Jamaica earthquake investigation presented at the last EGU2021-15456. We found that both approaches are very important. Actually, the statistical studies can provide proofs that at least some of the detected anomalies seem to be related to the earthquakes, while the single case studies permit us to explore deeply the details and the possible connections between the geolayers (lithosphere, atmosphere and ionosphere).In this presentation, we want to show an update of the statistical study of the atmosphere and ionosphere, together with a new statistical investigation of the seismic acceleration before M7.5+ global earthquakes.Finally, we demonstrate that it is essential to consider the earthquake not as a point source (that is the basic approximation), but in all its complexity, including its focal mechanism, fault rupture length and even other seismological constraints, in order to try to better understand the preparation phase of the earthquakes, and the reasons for their different behaviour. These studies give hope and fundamental (but not yet sufficient) tools for the possible achievement, one day, of earthquakes prediction capabilities.