Articles | Volume 16, issue 12
Development and technical paper
27 Jun 2023
Development and technical paper |  | 27 Jun 2023

Leveraging Google's Tensor Processing Units for tsunami-risk mitigation planning in the Pacific Northwest and beyond

Ian Madden, Simone Marras, and Jenny Suckale

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Cited articles

Abdolali, A. and Kirby, J. T.: Role of compressibility on tsunami propagation, J. Geophys. Res.-Oceans, 122, 9780–9794, 2017. a
Abdolali, A., Kadri, U., and Kirby, J. T.: Effect of water compressibility, sea-floor elasticity, and field gravitational potential on tsunami phase speed, Sci. Rep.-UK, 9, 1–8, 2019. a
Aida, I.: Numercal experiments for the tsunami propagation of the 1964 Niigata tsunami and 1968 Tokachi-Oki tsunami, B. Earthq. Res. I. Tokyo, 47, 673–700, 1969. a
Aida, I.: Numerical computational of a tsunami based on a fault origin model of an earthquake, J. Seismol. Soc. Jpn., 27, 141–154, 1974. a
Allgeyer, S. and Cummins, P. R.: Numerical tsunami simulation including elastic loading and seawater density stratification, Geophys. Res. Lett., 41, 2368–2375, 2014. a
Short summary
To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.