@article{7004e0c519a74b64986c3c6a2cb1a185,
title = "Tsunami Early Warning From Global Navigation Satellite System Data Using Convolutional Neural Networks",
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 to use less than 9 min of GNSS data to forecast the full tsunami waveforms over 6 hr at select locations, and obtain accurate forecasts on a test data set. Our training and test data consists of synthetic earthquakes and associated GNSS data generated for the Cascadia Subduction Zone 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.",
keywords = "GNSS, GeoClaw software, machine learning, neural network, synthetic ruptures, tsunami forecasting",
author = "Donsub Rim and Robert Baraldi and Liu, {Christopher M.} and LeVeque, {Randall J.} and Kenjiro Terada",
note = "Funding Information: Diego Melgar generated the hypothetical earthquakes used in this work and provided advice on the best use of GNSS data. The authors are also grateful to Xinsheng Qin for setting up and performing the GeoClaw simulations used as training and test data. Responding to the comments and questions of two anonymous referees led to significant improvements in this paper. RJL and CML were supported in part by Tohoku University. This research was sponsored by the Department of Energy Office of Science under the Advanced Scientific Computing Research John von Neumann Fellowship. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE‐NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Publisher Copyright: {\textcopyright} 2022 The Authors.",
year = "2022",
month = oct,
day = "28",
doi = "10.1029/2022GL099511",
language = "English",
volume = "49",
journal = "Geophysical Research Letters",
issn = "0094-8276",
publisher = "American Geophysical Union",
number = "20",
}