In this study, we analyzed signals transmitted by the U.S. Wide Area Augmentation System (WAAS) geostationary (GEO) satellites using the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithm in a simulated real-time scenario, to characterize the ionospheric response to the 24 August 2017 Falcon 9 rocket launch from Vandenberg Air Force Base in California. VARION is a real-time Global Navigation Satellites Systems (GNSS)-based algorithm that can be used to detect various ionospheric disturbances associated with natural hazards, such as tsunamis and earthquakes. A noise reduction algorithm was applied to the VARION-GEO solutions to remove the satellite-dependent noise term. Our analysis showed that the interactions of the exhaust plume with the ionospheric plasma depleted the total electron content (TEC) to a level comparable with nighttime TEC values. During this event, the geometry of the satellite-receiver link is such that GEO satellites measured the depleted plasma hole before any GPS satellites. We estimated that the ionosphere relaxed back to a pre-perturbed state after about 3 h, and the hole propagated with a mean speed of about 600 m/s over a region of 700 km in radius. We conclude that the VARION-GEO approach can provide important ionospheric TEC real-time measurements, which are not affected by the motion of the ionospheric pierce points (IPPs). Furthermore, the VARION-GEO measurements experience a steady noise level throughout the entire observation period, making this technique particularly useful to augment and enhance the capabilities of well-established GNSS-based ionosphere remote sensing techniques and future ionospheric-based early warning systems.

Global Navigation Satellite System (GNSS) is used in seismology to study the ground displacements as well as to monitor the ionospheric total electron content (TEC) perturbations following seismic events. The aim of this work is to combine these two observations in one real-time method based on the Total Variometric Approach (TVA) to include the GNSS real-time data stream in future warning systems and tsunami genesis estimation observing both, ground motion and TEC. Our TVA couples together the Variometric Approach for Displacement Analysis Stand-alone Engine (VADASE) with the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithms. We apply the TVA to the Mw 8.3 Illapel earthquake, that occurred in Chile on September 16, 2015, and we demonstrate the coherence of the earthquake ground shaking and the TEC perturbation by using the same GNSS data stream in a real-time scenario. Nominally, we also highlight a stronger kinetic energy released in the north of the epicenter and visible in both, the ground motion and the TEC perturbation detect at 30 s and around 9.5 min after the rupture respectively. The high spatial resolution of ionospheric TEC measurement seems to match with the extent of the seismic source. The GNSS data stream by TVA of both the ground and ionospheric measurement opens today new perspectives to real-time warning systems for tsunami genesis estimation.

The number of Android devices enabling access to raw GNSS (Global Navigation Satellite System) measurements is rapidly increasing, thanks to the dedicated Google APIs. In this study, the Xiaomi Mi8, the first GNSS dual-frequency smartphone embedded with the Broadcom BCM47755 GNSS chipset, was employed by leveraging the features of L5/E5a observations in addition to the traditional L1/E1 observations. The aim of this paper is to present two different smartphone applications in Geoscience, both based on the variometric approach and able to work in real time. In particular, tests using both VADASE (Variometric Approach for Displacement Analysis Stand-alone Engine) to retrieve the 3D velocity of a stand-alone receiver in real-time, and VARION (Variometric Approach for Real-Time Ionosphere Observations) algorithms, able to reconstruct real-time sTEC (slant total electron content) variations, were carried out. The results demonstrate the contribution that mass-market devices can offer to the geosciences. In detail, the noise level obtained with VADASE in a static scenario—few mm/s for the horizontal components and around 1 cm/s for the vertical component—underlines the possibility, confirmed from kinematic tests, of detecting fast movements such as periodic oscillations caused by earthquakes. VARION results indicate that the noise level can be brought back to that of geodetic receivers, making the Xiaomi Mi8 suitable for real-time ionosphere monitoring.

We investigate the oceanic and ionospheric response in New Caledonia‐New Zealand and Chile‐Argentina to the 15 January 2022 Hunga‐Tonga volcanic eruption. For the first time, we highlight a reversed response in the oceans and in the ionosphere in terms of the amplitudes. The sea‐surface fluctuations due to the passage of the atmospheric Lamb wave (i.e., air‐sea wave) were not remarkable while the related ionospheric perturbation was considerable. Reversely, the eruption‐induced tsunami (“regular” tsunami) caused major variations in sea‐surface heights (∼1 m near the volcano and ∼2 m along the Chilean coastline), whereas the associated ionospheric perturbation was quite small. The observed large‐amplitude ionospheric response due to Lamb waves propagation is difficult to explain, and the coupling between the Lamb wave and the ionosphere is not well‐understood yet. For the first time, we estimate the delay between the Lamb waves and their signatures in the ionosphere to be ∼12–20 min.

Earthquakes and tsunamis can trigger acoustic and gravity waves that could reach the ionosphere, generating electron density disturbances, known as traveling ionospheric disturbances. These perturbations can be investigated as variations in ionospheric total electron content (TEC) estimated through global navigation satellite systems (GNSS) receivers. The VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm is a well-known real-time tool for estimating TEC variations. In this context, the high amount of data allows the exploration of a VARION-based machine learning classification approach for TEC perturbation detection. For this purpose, we analyzed the 2015 Illapel earthquake and tsunami for its strength and high impact. We use the VARION-generated observations (i.e., dsTEC/dt) provided by 115 GNSS stations as input features for the machine learning algorithms, namely, Random Forest and XGBoost. We manually label time frames of TEC perturbations as the target variable. We consider two elevation cut-off time series, namely, 15° and 25°, to which we apply the classifier. XGBoost with a 15° elevation cut-off dsTEC/dt time series reaches the best performance, achieving an F1 score of 0.77, recall of 0.74, and precision of 0.80 on the test data. Furthermore, XGBoost presents an average difference between the labeled and predicted middle epochs of TEC perturbation of 75 s. Finally, the model could be seamlessly integrated into a real-time early warning system, due to its low computational time. This work demonstrates high-probability TEC signature detection by machine learning for earthquakes and tsunamis, that can be used to enhance tsunami early warning systems.