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Automated Approaches for Capturing Localized Tsunami Response—Application to the French Coastlines

Giles, Daniel; Gailler, Audrey; Dias, Frédéric; (2022) Automated Approaches for Capturing Localized Tsunami Response—Application to the French Coastlines. Journal of Geophysical Research: Oceans , 127 (6) , Article e2022JC018467. 10.1029/2022jc018467. Green open access

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Abstract

Local bathymetry and onshore features can have a substantial effect on the spatial variability of the hazard from an incoming tsunami. In a warning context, being able to provide localized tsunami forecasts at strategic locations would therefore help mitigate the damage. Despite the recent advancements in computing powers and the development of highly efficient tsunami codes, capturing this local variability can oftentimes be infeasible in a warning setting. Traditional high-resolution simulations which can capture these localized effects are often too costly to run “on-the-fly.” Alternative approaches that capture the localized response to an incoming tsunami, which are based upon using the maximum wave heights from a computationally cheap regional forecast, are developed here. These alternative approaches are envisaged to aid in a warning center's ability at providing extremely rapid localized forecasts. The approaches focus upon two different methods: transfer functions and machine learning techniques. The transfer function is based upon a recent extension to the established Green's Law. The extended version introduces local amplification parameters, with the aim of capturing the neglected localized effects. An automated approach which optimizes for these local amplification parameters is outlined and the performance of the transfer function is explored. A machine learning model is also trained and used to predict the localized tsunami hazard. Its performance is compared to the extended Green's Law approach for a site along the French coast. These developed methods showcase promising techniques that a tsunami warning center could use to provide high-resolution warnings.

Type: Article
Title: Automated Approaches for Capturing Localized Tsunami Response—Application to the French Coastlines
Open access status: An open access version is available from UCL Discovery
DOI: 10.1029/2022jc018467
Publisher version: https://doi.org/10.1029/2022JC018467
Language: English
Additional information: © 2022 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/).
Keywords: tsunami, transfer functions, machine learning, Green's Law
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10150430
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