Leveraging GNSS data for tsunami early warning systems
Creators: name: Federica Fuso
Publisher: name: Zenodo
Year: 2024

description: Natural hazards such as earthquakes and tsunamis can trigger acoustic and gravity waves (AGWs) that reach the ionosphere generating electron density disturbances (i.e., Travelling Ionospheric Disturbances - TIDs). These can be investigated as variations in the ionospheric total electron content (TEC) measurements, collected by Global Navigation Satellite Systems (GNSS) receivers. 

The VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm is able to estimate TEC variations in real-time since it is based on single-time differences of geometry-free combinations of GNSS carrier-phase measurements [1,2]. VARION is routinely used to characterize the ionospheric response to several natural hazards. 

In this work, we introduce two innovative applications of the VARION algorithm, highlighting its potential to enhance existing warning systems.

VARION was, for the first time, applied to detecting TEC variations induced by small tsunamis in the Mediterranean Sea. In detail, the Mw 7.0 Samos earthquake (37.8360° N 26.8080° E), which occurred on 30 October 2020, was analyzed. We employ 10 GNSS receivers from the Turkish (TUSAGA-Aktif) and Greek (HEPOS) networks, considering raw and filtered (0.1-3 mHz bandpass and polynomial filters) VTEC time series. Given the waveforms, the ionospheric arrival times (about 40 minutes after the event), the spectral features (showing a peak frequency of 1-2 mHz), the TEC pattern in the hodochrones, and the apparent velocity (about 208 m/s), we attribute the observed phenomenon to the internal gravity waves (IGWs) propagation. The comparison of IGWs arrival times in the ionosphere with tsunami wave arrivals at tide gauges reveals that optimal observation geometries of ionospheric TEC can help detect the tsunami's arrival before it reaches the Kos and Hraklreio coastlines. This highlights the potential of TEC variations to enhance tsunami early warning systems, especially in the less-studied Mediterranean region.

Recently, we also explored the possibility of a VARION-based machine learning classification approach to detect TEC perturbation induced by earthquakes and tsunamis. Specifically, we focused on the 2015 Illapel earthquake and tsunami because of its significant ionospheric response.

Random Forest (RF) and XGBoost (XGB) are employed using VARION-core observations (i.e., dsTEC/dt) as input features for binary classification using supervised machine learning. TEC perturbations time frames are manually labelled, and classifiers are applied to 15° and 25° elevation cut-off time series. XGBoost with a 15° elevation cut-off dsTEC/dt time series reaches the best performance: an F1 score of 0.77, recall of 0.74, and precision of 0.80, with 74.09% of TPs, 98.49% of TNs, 25.91% of FNs, and 1.51% of FPs on testing data [3].

This model shows a 75-second average deviation in predicting perturbation time frames for testing links (i.e., 5 steps in the 15-second time series intervals). TIDs detection lasts 2-3 minutes, highlighting its computational efficiency for effective early warning systems. Finally, it relies only on VARION-generated real-time TEC time series (dsTEC/dt), enabling operational real-time settings using GNSS data.

In conclusion, this research highlights the importance of using GNSS ionospheric real-time TEC data to improve tsunami hazard estimation. It shows that even small amplitude TEC variations can provide critical early warning information for minor events (e.g., in the Mediterranean Sea) and that machine learning can reliably detect TEC signatures for larger earthquakes and tsunamis.

 

References

[1] Ravanelli, M., Occhipinti, G., Savastano, G., Komjáthy, A., Shume, E., & Crespi, M.G. (2021). GNSS total variometric approach: first demonstration of a tool for real-time tsunami genesis estimation. Scientific Reports, 11(1), 3114. 

[2] Savastano, G., Komjathy, A., Verkhoglyadova, O., Mazzoni, A., Crespi, M., Wei, Y., & Mannucci, A. J. (2017). Real-time detection of tsunami ionospheric disturbances with a stand-alone GNSS receiver: A preliminary feasibility demonstration. Scientific reports, 7(1), 46607.

 [3] Fuso, F., Crocetti, L., Ravanelli, M. & Soja, B. (2024). Machine learning-based detection of TEC signatures related to earthquakes and tsunamis: the 2015 Illapel case study. GPS Solutions, 28(3), 106.