Tsunami Early Warning from Global Navigation Satellite System Data using Convolutional Neural Networks

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1029/2022GL099511. This is version 2 of this Preprint.

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Authors

Donsub Rim , Robert Baraldi, Christopher M Liu, Randall J LeVeque, Kenjiro Terada

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.

DOI

https://doi.org/10.31223/X5R34S

Subjects

Earth Sciences, Geophysics and Seismology, Hydrology, Oceanography

Keywords

tsunami forecasting, machine learning, Neural Network, GNSS, synthetic ruptures, GeoClaw software

Dates

Published: 2022-09-29 10:53

Last Updated: 2022-10-01 12:11

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License

CC BY Attribution 4.0 International