Abstract.
We investigate the potential of using Global Navigation Satellite System (GNSS) observations to directly forecast full tsunami waveforms in real time. We train convolutional neural networks (CNNs) to use less than 9 minutes of GNSS data to forecast the full tsunami waveforms over 6 hours at select locations, and obtain accurate forecasts on a test dataset. Our training and test data consists of synthetic earthquakes and associated GNSS data generated for the Cascadia Subduction Zone (CSZ) using the MudPy software, and corresponding tsunami waveforms in Puget Sound computed using GeoClaw. We use the same suite of synthetic earthquakes and waveforms as in earlier work where tsunami waveforms were used for forecasting, and provide a comparison. We also explore varying the number of GNSS stations, their locations, and their observation durations.
Plain Language Summary.
Producing rapid real-time forecasts for tsunamis in the first few minutes of an earthquake is a challenging problem. Accurate forecasts often rely on direct measurements of the tsunami, which are only available at sparse locations, and only after the tsunami has passed the sensors. Real-time numerical modeling of the tsunami is also time consuming. This work attempts to bypass these difficulties by considering a model that can forecast tsunami wave heights based only on Global Navigation Satellite System (GNSS) data, which is available within minutes from an extensive network of stations. We present some initial results using this approach for hypothetical tsunamis originating from the Cascadia Subduction Zone, with forecast locations in Puget Sound. We show that this approach gives comparable results to earlier work based on observing tsunami waveforms for 30 or 60 minutes, but now using only a few minutes of GNSS data. We explore varying the number of GNSS stations and find that the model yields accurate forecasts when as few as 20 GNSS stations are used, and outperforms our previous model when additional stations are used. The model performs well even when only the initial 4 minutes of GNSS data is used.
Journal: DOI 10.1029/2022GL099511
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bibtex entry:
article{RimBaraldi2022,
author = {D. Rim and R. Baraldi and C. M. Liu and R. J. LeVeque and
K. Terada},
title = {Tsunami Early Warning From Global Navigation Satellite System Data
Using Convolutional Neural Networks},
journal = {Geophysical Research Letters},
volume = {49},
year = {2022}
pages = {e2022GL099511},
doi = {https://doi.org/10.1029/2022GL099511}
}