Harnessing AI in GNSS remote sensing: A Deep Learning framework for Real-Time Detection of Ionospheric Perturbations triggered by earthquakes and tsunamis

Global Navigation Satellite System (GNSS) Ionospheric Seismology explores the ionospheric response to earthquakes and tsunamis, which generate Traveling Ionospheric Disturbances (TIDs) detectable through GNSS-derived Total Electron Content (TEC) observations. Real-time TID identification can offer a transformative approach to tsunami detection, enhancing tsunami early warning systems (TEWS) by extending coverage to open-ocean regions where traditional buoy-based systems are limited. Scalable and automated TID detection is therefore essential for augmenting TEWS capabilities.

In this work, we present a novel deep learning framework for real-time TID detection [3]. Leveraging Gramian Angular Difference Fields (GADFs), we transform TEC time-series data, retrieved via the VARION algorithm [1, 2], into images. Images were categorized based on ground truth: those overlapping labeled TID ranges were classified as TIDs, while others represented normal ionospheric TEC. This approach offers multiple advantages: (1) it preserves temporal dependencies through the sequential structure of GADFs, (2) allows reconstruction of original time-series data via its bijective nature, and (3) highlights temporal correlations within the data. Furthermore, GADFs encode temporal information directly in the images, making the method robust to missing data and suitable for image-based deep learning models in anomaly detection. Additionally, GADFs produce visually interpretable differences across classes, enhancing their utility.

We evaluated our framework using data from four tsunamigenic earthquakes in the Pacific Ocean: the 2010 Maule earthquake, the 2011 Tohoku earthquake, the 2012 Haida Gwaii earthquake, and the 2015 Illapel earthquake. The first three events were used for model training, while out-of-sample validation was performed on the Illapel earthquake to assess real-world applicability.

A single ResNet (50-layer) model was trained for TID detection, incorporating both anomalous (TID-containing) and normal data to ensure exposure to diverse scenarios. TEC data streams were processed chronologically in 60-minute windows, generating GADF images that the model classified as anomalous or normal. Predicted anomalies were concatenated into sequences and compared against ground truth. To further enhance performance, we integrated a false positive mitigation strategy, based on the likelihood of a TID at each time step, significantly reducing false positives. The model achieved an F1 score of 91.7% and a recall of 84.6%, demonstrating its strong potential for operational use in real-time applications.

By embedding deep learning into real-time GNSS-TEC analysis, this research represents a significant advancement in the use of TEC data for TEWS and underscores the potential of deep learning in geodetic time series analysis.

 References

[1] Savastano, G. et al. (2017). “Real-time detection of tsunami ionospheric disturbances with a stand-alone GNSS receiver: A preliminary feasibility demonstration”. Scientific reports, 7(1), 46607.

[2] Ravanelli, M., et al. "GNSS total variometric approach: first demonstration of a tool for real-time tsunami genesis estimation." Scientific reports 11.1 (2021): 3114.

[3] Ravanelli, M. et al. "Exploring AI progress in GNSS remote sensing: A deep learning based framework for real-time detection of earthquake and tsunami induced ionospheric perturbations." Radio Science 59.9 (2024): 1-18.