Ship-based GNSS ionospheric observations for the detection of tsunamis with deep learning

Tsunami detection and forecasting require observations from open-ocean sensors. It is well known that tsunamis can generate internal gravity waves that propagate through the ionosphere from the earthquake center along with the tsunami wave. These disturbances can be detected by Global Navigation Satellite Systems (GNSS) receivers. The VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm has been successfully applied to detecting traveling ionospheric perturbations (TIDs) in several real-time scenarios, and it has also been successfully demonstrated that this algorithm is suitable for moving systems such as ship-based GNSS receivers. We present analyses of GNSS data collected from ships and examine the potential of a ship-based GNSS network for the ionospheric detection of tsunamis. 

In this project, we focused on the detection of tsunami signals from the TIDs using deep learning methods. Benefiting from the large amount of data from widely distributed GNSS permanent stations, we developed a prototype convolutional neural network for tsunami detection, achieving highly accurate prediction scores on the validation and test data. We used the observations coming from our 10-ship pilot network real-time GNSS system from the Pacific ocean to detect the TIDs related to the 2015 Illapel, Chile earthquake and tsunami. Using our algorithm in a post-processing mode we found that our ships successfully detected the ionospheric tsunami signal even though there was no detectable sea-surface height perturbation for the ship. Comparing the performance using our deep learning method with other anomaly detection approaches in a real-time scenario, we found that our approach works very efficiently with the pre-trained model. The results of our study, although preliminary, are very encouraging and we conclude that ships can be cost-effective real-time tsunami early-warning sensors. Given that there are thousands of existing ships in the Pacific Ocean, this is a promising opportunity to improve hazard mitigation.