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 19 September 2021, La Palma Cumbre Vieja Volcano started an eruption classified as Volcanic Explosive Index (VEI) 3. In this study, at least the six months prior to such an event have been investigated to search for possible lithosphere–atmosphere–ionosphere bottom-up interactions. The lithosphere has been analysed in terms of seismicity getting advantages from the high-density local seismic network. Possible atmospheric alterations related to the volcano emissions or release of gases due to the uplift of the magmatic chamber have been searched in SO2, aerosol, dimethyl sulphide, and CO. The magnetic field on Earth’s surface has been studied by ground geomagnetic observatories. The status of the ionosphere has been investigated with two satellite missions: China Seismo Electromagnetic Satellite (CSES) and European Space Agency Swarm constellation, with Total Electron Content (TEC) retrieved from global maps. We identified a temporal migration of the seismicity from November 2020 at a depth of 40 km that seems associable to magma migration, firstly to a deep chamber at about 15 km depth and in the last 10 days in a shallow magma chamber at less than 5 km depth. The atmospheric composition, ground geomagnetic field, and ionosphere showed anomalies from more than three months before the eruption, suggesting a possible influence from the bottom geo-layers to the upper ones. CSES-01 detected an increase of electron density, confirmed by TEC data, and alterations of vertical magnetic field on ground Guimar observatory that are temporal compatible with some volcanic low seismic activity (very likely due to the magma uplift), suggesting an eventual electromagnetic disturbance from the lithosphere to the ionosphere. A final increase of carbon monoxide 1.5 months before the eruption with unusually high values of TEC suggests the last uplifting of the magma before the eruption, confirmed by a very high shallow seismicity that preceded the eruption by ten days. This work underlines the importance of integrating several observation platforms from ground and overall space to understand geophysics better, and, in particular, the natural hazard affecting our planet.
In this paper, based on non-negative matrix factorization (NMF), we analyzed the ionosphere magnetic field data of the Swarm Alpha satellite before the 2016 (Mw = 7. 8) Ecuador earthquake (April 16, 0.35°N, 79.93°W), including the whole data collected under quiet and disturbed geomagnetic conditions. The data from each track were decomposed into basis features and their corresponding weights. We found that the energy and entropy of one of the weight components were more concentrated inside the earthquake-sensitive area, which meant that this weight component was more likely to reflect the activity inside the earthquake-sensitive area. We focused on this weight component and used five times the root mean square (RMS) to extract the anomalies. We found that for this weight component, the cumulative number of tracks, which had anomalies inside the earthquake-sensitive area, showed accelerated growth before the Ecuador earthquake and recovered to linear growth after the earthquake. To verify that the accelerated cumulative anomaly was possibly associated with the earthquake, we excluded the influence of the geomagnetic activity and plasma bubble. Through the random earthquake study and low-seismicity period study, we found that the accelerated cumulative anomaly was not obtained by chance. Moreover, we observed that the cumulative Benioff strain S, which reflected the lithosphere activity, had acceleration behavior similar to the accelerated cumulative anomaly of the ionosphere magnetic field, which suggested that the anomaly that we obtained was possibly associated with the Ecuador earthquake and could be described by one of the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) models.
In order to monitor temporal and spatial crustal activities associated with earthquakes, ground- and satellite-based monitoring systems have been installed in China since the 1990s. In recent years, the correlation between monitoring strain anomalies and local major earthquakes has been verified. In this study, we further evaluate the possibility of strain anomalies containing earthquake precursors by using Receiver Operating Characteristic (ROC) prediction. First, strain network anomalies were extracted in the borehole strain data recorded in Western China during 2010–2017. Then, we proposed a new prediction strategy characterized by the number of network anomalies in an anomaly window, Nano, and the length of alarm window, Talm. We assumed that clusters of network anomalies indicate a probability increase of an impending earthquake, and consequently, the alarm window would be the duration during which a possible earthquake would occur. The Area Under the ROC Curve (AUC) between true predicted rate, tpr, and false alarm rate, fpr, is measured to evaluate the efficiency of the prediction strategies. We found that the optimal strategy of short-term forecasts was established by setting the number of anomalies greater than 7 within 14 days and the alarm window at one day. The results further show the prediction strategy performs significantly better when there are frequent enhanced network anomalies prior to the larger earthquakes surrounding the strain network region. The ROC detection indicates that strain data possibly contain the precursory information associated with major earthquakes and highlights the potential for short-term earthquake forecasting.
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