Lithosphere-Atmosphere-Ionosphere coupling models have been widely studied and applied to earthquakes and their potential precursors. Recent evidence has been found that electromagnetic phenomena related to earthquakes might produce ionospheric anomalies even before their occurrence. This study analyzes their correlation focusing on the ionospheric scintillation associated to seismic activities in 2022 in the area around the Coral Sea. Among all the ionospheric indicators, this study analyzes the S4scintillation index from COSMIC-2 GNSS Radio Occultation (GNSS-RO), to estimate the ionospheric perturbation and identify anomalies. Two cases of earthquakes in 2022 with magnitudes larger than 6 are presented showing the positive correlation between the ionospheric scintillation and earthquake precursors.

The ionospheric scintillation can be explained as the fluctuations in the phase and intensity of electromagnetic rays after crossing the ionosphere. Rino’s theory was proposed in 1979 to quantify this scintillation, and subsequent models appeared to predict its characteristics. One of them is the WideBand ionospheric scintillation Model (WBMOD) from 1984. This study aims to provide a neural network that emulates the behavior of WBMOD model, by learning from phase and intensity scintillation data gathered from several ESA projects. By using the Rino’s power-law phase-screen ionospheric scintillation theory, the values of the height-integrated irregularities strength (CkL) and the slope of its PSD (q) can be obtained from the physically measured S4and σϕ. So, using this data, two neural networks are presented which obtains results that fit well with the WBMOD expected values.

From 1998 to 2022, Earthquakes have caused more than 850 thousand deaths and US$ 700 billion in economic losses. At this time, there is no reliable precursor to predict them. Many studies have attempted to link Land Surface Temperature (LST) anomalies to earthquake occurrence. This study uses long-term datasets from FengYun-2F/VISSR (Visible and Infrared Spin Scan Radiometer) sensors [1] between 2014 and 2022 to analyze the extent of LST anomalies and their potential link to earthquakes. More than 14,000 land and coastal earthquakes of magnitude higher than M4 have been studied between [+40°, +140°] longitude. Three methods frequently used in the literature, the Standard Deviation (STD), the Interquartile (IQT), and the Z-Score techniques have been implemented to detect LST anomalies on temporal series. Other parameters, such as the confusion matrix and the Receiver Operating Curves (ROC), have also been computed to assess and improve their performance and select the optimum correlation threshold. A positive LST anomaly is typically found before the earthquakes, followed by an LST decrease after the event.

Recent studies have shown possible signatures or precursors of seismic activity in the ionosphere. Our group is focusing on ionospheric scintillation associated with seismic activity. By the time this study was conducted, the sudden volcanic eruption in the Spanish La Palma island, starting on September 19th, opened a possibility to study the impact of this exceptional, well time-defined seismic event on the iono-sphere and the radio-wave propagation through it. A complete study measuring scintillation on GNSS signals and its correlation to seismic activity is presented in this extended abstract. In particular, scintillation data from two ground stations in the Canary Islands, NASA CYGNSS GNSS-Reflectometry and Spire GNSS Radio-Occultation measurements have been used. A linear correlation analysis has been conducted between S4, and the earthquakes generated energy in 6 h intervals. Small, and slightly positive regression coefficients have been found with almost all methods.

Annually, earthquakes cause human and material losses. For instance, between 1998 and 2018, 846 thousand deaths and about US$ 661 billion of economic losses were recorded due to earthquakes. Currently, there is no clear precursor to forecast earthquakes. However, numerous investigations have attempted to find precursor proxies based on Land Surface Temperature (LST) anomalies. In this study, a big database collected from GOES/ABI instrument during the full year 2020 has been used to calculate the LST anomalies in the earthquakes zones. A total of 1350 earthquakes of Mw ≥ 4 were studied in 2020. Two methods commonly used in the literature, the interquartile method, and the standard deviation of the time series, have been applied to detect LST anomalies. The confusion matrix, some figures of merit, and the receiver operating characteristic curve have been used to evaluate and enhance the performance of the methods and choose the optimum decision threshold. A positive anomaly is usually found before the earthquakes, followed by an LST decrease after the event.

Several factors may induce perturbations on the ionospheric plasma, changing its average electron density and creating small-scale irregularities, changing its shape and altitude. Solar irradiance and space weather are some of the main factors affecting the ionosphere. They produce a seasonal and daily dependence, modulated by the solar cycle, with more ionospheric activity during periods of higher solar activity. Recent studies shows that another source of perturbations for the ionosphere may be related to internal Earth parameters as seismic activity, in particular, earthquakes. In the period before an earthquake, rocks in the lithosphere are subjected to pressures and movements that may create variations of electromagnetic fields and low frequency waves interacting with the ionosphere. In this work, the ionospheric scintillation intensity index or S4 is estimated from GNSS-R data collected by NASA CYGNSS, and it is correlated with earthquakes events in 2020. Furthermore, it is compared with plasma fluctuation indices measured by ESA Swarm satellites. Two earthquakes in 2020 with magnitudes larger than 7 in the central America region are shown in this work.