33 research outputs found
"Detection of tsunami induced ionospheric perturbation with ship-based GNSS measurements: 2010 Maule tsunami case study
No full textThe VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm has been
successfully applied to TIDs (Travelling ionospheric disturbances) detection in several real-time
scenarios [1, 2]. VARION, thus, estimates sTEC (slant total electron content) variations starting from
the single time differences of geometry-free combinations of GNSS carrier-phase measurements.
This feature makes VARION suitable to also leverage GNSS observations coming from moving
receivers such as ship-based GNSS receivers: the receiver motion does not affect the sTEC
estimation process.
The aim of this work is to use the observations coming from two GNSS receivers installed on a ship
moving near Kauai Island in the Hawaiian archipelago to detect the TIDs connected to the 2010
Maule earthquake and tsunami [3]. Indeed, this earthquake triggered a tsunami that affected all
the Pacific region and that reached the Hawaiian islands after about 15 hours. All our analysis was
carried out in post-processing, but simulated a real-time scenario: only the data available in real
time were used.
In order to get a reference, the ship-based sTEC variations were compared with the ones coming
from GNSS permanent stations situated in the Hawaiian Islands. In particular, if we considered the
same satellite, the same TID is detected by both ship and ground receivers. As expected, the shipbased
sTEC variations are a little bit noisier since they are coming from a kinematic platform.
Hence, the results, although preliminary, are very encouraging: the same TIDs is detected both
from the sea (ships) and land (permanent receivers). Therefore, the VARION algorithm is also able
to leverage observations coming from ship-based GNSS receivers to detect TIDs in real-time.
In conclusion, we firmly believe that the application of VARION to observation coming from shipbased
GNSS receivers could really represent a real-time and cost-effective tool to enhance tsunami
early warning systems, without requiring the installation of complex infrastructures in open sea.
References
[1] Giorgio Savastano, Attila Komjathy, Olga Verkhoglyadova, Augusto Mazzoni, Mattia Crespi, Yong
Wei, and Anthony J Mannucci, “Real-time detection of tsunami ionospheric disturbances with a
stand-alone gnss receiver: A preliminary feasibility demonstration, ”Scientific reports, vol. 7, pp.
46607, 2017.
[2] Giorgio Savastano, Attila Komjathy, Esayas Shume, Panagiotis Vergados, Michela Ravanelli, Olga
Verkhoglyadova, Xing Meng, and Mattia Crespi, “Advantages of geostationary satellites for
ionospheric anomaly studies: Ionospheric plasma depletion following a rocket launch,”Remote
Sensing, vol. 11, no. 14, pp. 1734, 2019
[3] https://earthquake.usgs.gov/earthquakes/eventpage/official20100227063411530_30/executiv
Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations
No full textThe ionosphere is a part of the upper atmosphere that is a threat to GNSS and satellite telecommunication systems. In this chapter, we will dive into the GNSS real-time monitoring of ionospheric irregularities and TEC perturbations, with a focus on the detection of small- and medium-scale traveling ionospheric disturbances (TIDs) for natural hazard applications. We will describe the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithm, which is capable of estimating TEC variations in real time, and it was used to detect tsunami-induced TIDs. In particular, the analytical and physical implications of applying the VARION algorithm both to GNSS dual-frequency MEO (medium Earth orbit) and GEO (geostationary orbit) satellites will be provided, thus highlighting its relevance for natural hazard early warning systems and real-time monitoring of ionospheric irregularities
Real-time geophysical applications with Android GNSS raw measurements
No full textThe 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
A joint use of GNSS GEO and MEO satellites for earthquake and tsunami induced TIDs analysis: application to recent relevant events in the Pacific area
No full textTids Detection from Ship-Based GNSS Receiver. First Test on 2010 Maule Tsunami
No full textThe VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm has been successfully applied several times to TIDs (Travelling ionospheric disturbances) detection in a real-time scenario. VARION is, thus, able to estimate sTEC variations in real time and it can be applied on ship-based GNSS receiver since it is based on geometry free combination: the receiver motion does not affect the sTEC estimation process. This work is a feasibility study on the possibility to use data from ship-based GNSS receiver to detect
TIDs. In particular, data of the February 27, 2010, MW 8.8 Chilean (Maule) earthquake and tsunami were analysed.
The preliminary results show that the same TIDs are detected both from the sea (ships) and land. In conclusion, ship-based GNSS receivers could represent a real-time and cost-effective tool to enhance tsunami early warning systems, without requiring the installation of complex infrastructures in open sea
Deep Learning Driven Detection of Tsunami Related Internal Gravity Waves: a path towards open-ocean natural hazards detection
No full textTsunamis can trigger internal gravity waves (IGWs) in the ionosphere, perturbing the Total Electron Content (TEC) - referred to as Traveling Ionospheric Disturbances (TIDs) that are detectable through the Global Navigation Satellite System (GNSS). The GNSS are constellations of satellites providing signals from Earth orbit - Europe's Galileo, the United States' Global Positioning System (GPS), Russia's Global'naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) and China's Bei-Dou. The real-time detection of TIDs provides an approach for tsunami detection, enhancing early warning systems by providing open-ocean coverage in geographic areas not serviceable by buoy-based warning systems. Large volumes of the GNSS data is leveraged by deep learning, which effectively handles complex non-linear relationships across thousands of data streams. We describe a framework leveraging slant total electron content (sTEC) from the VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm by Gramian Angular Difference Fields (from Computer Vision) and Convolutional Neural Networks (CNNs) to detect TIDs in near-real-time. Historical data from the 2010 Maule, 2011 Tohoku and the 2012 Haida-Gwaii earthquakes and tsunamis are used in model training, and the later-occurring 2015 Illapel earthquake and tsunami in Chile for out-of-sample model validation. Using the experimental framework described in the paper, we achieved a 91.7% F1 score. Source code is available at: https://github.com/vc1492a/tidd. Our work represents a new frontier in detecting tsunami-driven IGWs in open-ocean, dramatically improving the potential for natural hazards detection for coastal communities
A Deep Learning Approach for Detection of Internal Gravity Waves in Earth’s Ionosphere
No full textTsunamis can trigger internal gravity waves (IGWs) in the ionosphere, perturbing the Total Electron Content (TEC) - referred to as Traveling Ionospheric Disturbances (TIDs) that are detectable through the Global Navigation Satellite System (GNSS). The real-time detection of TIDs provides an approach for tsunami detection, enhancing early warning systems by providing open-ocean coverage in geographic areas not serviceable by buoy-based warning systems. Large volumes of the GNSS data is leveraged by deep learning, which effectively handles complex non-linear relationships across thousands of data streams. We describe a framework leveraging slant total electron content (sTEC) from the VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm by Convolutional Neural Networks (CNNs) to detect TIDs in near-real-time. Historical data from the 2010 Maule, 2011 Tohoku and the 2012 Haida-Gwaii earthquakes and tsunamis are used in model training, and the later-occurring 2015 Illapel earthquake and tsunami in Chile for out-of-sample model validation. Using the experimental framework described in the paper, we achieved a 91.7% F1 score. Source code is available at: https://github.com/vc1492a/tidd
Exploring AI Progress in GNSS Remote Sensing: A Deep Learning Based Framework for Real‐Time Detection of Earthquake and Tsunami Induced Ionospheric Perturbations
No full textGlobal Navigation Satellite System Ionospheric Seismology investigates the ionospheric response to earthquakes and tsunamis. These events are known to generate Traveling Ionospheric Disturbances (TIDs) that can be detected through GNSS-derived Total Electron Content (TEC) observations. Real-time TID identification provides a method for tsunami detection, improving tsunami early warning systems (TEWS) by extending coverage to open-ocean regions where buoy-based warning systems are impractical. Scalable and automated TID detection is, hence, essential for TEWS augmentation. In this work, we present an innovative approach to perform automatic real-time TID monitoring and detection, using deep learning insights. We utilize Gramian Angular Difference Fields (GADFs), a technique that transforms time-series into images, in combination with Convolutional Neural Networks (CNNs), starting from VARION (Variometric Approach for Real-time Ionosphere Observation) real-time TEC estimates. We select four tsunamigenic earthquakes that occurred in the Pacific Ocean: the 2010 Maule earthquake, the 2011 Tohoku earthquake, the 2012 Haida-Gwaii, the 2015 Illapel earthquake. The first three events are used for model training, whereas the out-of-sample validation is performed on the last one. The presented framework, being perfectly suitable for real-time applications, achieves 91.7% of F1 score and 84.6% of recall, highlighting its potential. Our approach to improve false positive detection, based on the likelihood of a TID at each time step, ensures robust and high performance as the system scales up, integrating more data for model training. This research lays the foundation for incorporating deep learning into real-time GNSS-TEC analysis, offering a joint and substantial contribution to TEWS progression.Global Navigation Satellite System Ionospheric Seismology investigates how the ionosphere responds to earthquakes and tsunamis, detecting TIDs through GNSS-derived TEC observations. Real-time TID identification aids tsunami detection, enhancing early warning systems by extending coverage to open-ocean regions. Automated TID detection is crucial for early warning system improvement. In this study, we propose an innovative approach using deep learning insights to perform automatic real-time TID monitoring and detection. We leverage GADFs and CNNs with VARION real-time TEC estimates. We train the model on four tsunamigenic earthquakes in the Pacific Ocean and validate it on an out-of-sample event. The framework achieves promising performance metrics, highlighting its potential for real-time applications. Our approach improves false positive detection, ensuring robustness and scalability as the system integrates more data for training. This research paves the way for integrating deep learning into real-time GNSS-TEC analysis, contributing significantly to the advancement of early warning systems.The proposed deep learning-based framework can detect earthquake and tsunami induced ionospheric perturbations in Total Electron Content observations We used a multi-event approach, focusing on the Pacific area, to train and to test the framework, achieving 91% of F1 score and 84% of recall The framework is well-suited for real-time applications, making it readily deployable to enhance tsunami early warning system
Machine learning-based detection of TEC signatures related to earthquakes and tsunamis: the 2015 Illapel case study
No full textEarthquakes 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 degrees and 25 degrees, to which we apply the classifier. XGBoost with a 15 degrees 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
