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:
this https URL 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.

使用实时检测行程海域重建大气不稳定性（TIDs）的方法提升海啸预警系统并实时检测海啸引发的内部重力波。