This is a Preprint and has not been peer reviewed.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.
Downloads
Authors
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 18:53
Last Updated: 2022-10-01 20:11
There are no comments or no comments have been made public for this article.