Jacob Bortnik

Valentino Constantinou, Michela Ravanelli, Hamlin Liu et al.

Tsunamis 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 BeiDou. 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.

S. A. Reddy, C. Forsyth, A. Aruliah et al.

In this study we present AI Prediction of Equatorial Plasma Bubbles (APE), a machine learning model that can accurately predict the Ionospheric Bubble Index (IBI) on the Swarm spacecraft. IBI is a correlation ($R^2$) between perturbations in plasma density and the magnetic field, whose source can be Equatorial Plasma Bubbles (EPBs). EPBs have been studied for a number of years, but their day-to-day variability has made predicting them a considerable challenge. We build an ensemble machine learning model to predict IBI. We use data from 2014-22 at a resolution of 1sec, and transform it from a time-series into a 6-dimensional space with a corresponding EPB $R^2$ (0-1) acting as the label. APE performs well across all metrics, exhibiting a skill, association and root mean squared error score of 0.96, 0.98 and 0.08 respectively. The model performs best post-sunset, in the American/Atlantic sector, around the equinoxes, and when solar activity is high. This is promising because EPBs are most likely to occur during these periods. Shapley values reveal that F10.7 is the most important feature in driving the predictions, whereas latitude is the least. The analysis also examines the relationship between the features, which reveals new insights into EPB climatology. Finally, the selection of the features means that APE could be expanded to forecasting EPBs following additional investigations into their onset.