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Probabilistic quantification of tsunami current hazard using statistical emulation

In this paper, statistical emulation is shown to be an essential tool for the end-to-end physical and numerical modelling of local tsunami impact, i.e. from the earthquake source to tsunami velocities and heights. In order to surmount the prohibitive computational cost of running a large number of s...

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Detalles Bibliográficos
Autores principales: Gopinathan, Devaraj, Heidarzadeh, Mohammad, Guillas, Serge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364761/
https://www.ncbi.nlm.nih.gov/pubmed/35153568
http://dx.doi.org/10.1098/rspa.2021.0180
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author Gopinathan, Devaraj
Heidarzadeh, Mohammad
Guillas, Serge
author_facet Gopinathan, Devaraj
Heidarzadeh, Mohammad
Guillas, Serge
author_sort Gopinathan, Devaraj
collection PubMed
description In this paper, statistical emulation is shown to be an essential tool for the end-to-end physical and numerical modelling of local tsunami impact, i.e. from the earthquake source to tsunami velocities and heights. In order to surmount the prohibitive computational cost of running a large number of simulations, the emulator, constructed using 300 training simulations from a validated tsunami code, yields 1 million predictions. This constitutes a record for any realistic tsunami code to date, and is a leap in tsunami science since high risk but low probability hazard thresholds can be quantified. For illustrating the efficacy of emulation, we map probabilistic representations of maximum tsunami velocities and heights at around 200 locations about Karachi port. The 1 million predictions comprehensively sweep through a range of possible future tsunamis originating from the Makran Subduction Zone (MSZ). We rigorously model each step in the tsunami life cycle: first use of the three-dimensional subduction geometry Slab2 in MSZ, most refined fault segmentation in MSZ, first sediment enhancements of seabed deformation (up to 60% locally) and bespoke unstructured meshing algorithm. Owing to the synthesis of emulation and meticulous numerical modelling, we also discover substantial local variations of currents and heights.
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spelling pubmed-83647612022-02-11 Probabilistic quantification of tsunami current hazard using statistical emulation Gopinathan, Devaraj Heidarzadeh, Mohammad Guillas, Serge Proc Math Phys Eng Sci Research Articles In this paper, statistical emulation is shown to be an essential tool for the end-to-end physical and numerical modelling of local tsunami impact, i.e. from the earthquake source to tsunami velocities and heights. In order to surmount the prohibitive computational cost of running a large number of simulations, the emulator, constructed using 300 training simulations from a validated tsunami code, yields 1 million predictions. This constitutes a record for any realistic tsunami code to date, and is a leap in tsunami science since high risk but low probability hazard thresholds can be quantified. For illustrating the efficacy of emulation, we map probabilistic representations of maximum tsunami velocities and heights at around 200 locations about Karachi port. The 1 million predictions comprehensively sweep through a range of possible future tsunamis originating from the Makran Subduction Zone (MSZ). We rigorously model each step in the tsunami life cycle: first use of the three-dimensional subduction geometry Slab2 in MSZ, most refined fault segmentation in MSZ, first sediment enhancements of seabed deformation (up to 60% locally) and bespoke unstructured meshing algorithm. Owing to the synthesis of emulation and meticulous numerical modelling, we also discover substantial local variations of currents and heights. The Royal Society Publishing 2021-06 2021-06-09 /pmc/articles/PMC8364761/ /pubmed/35153568 http://dx.doi.org/10.1098/rspa.2021.0180 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Research Articles
Gopinathan, Devaraj
Heidarzadeh, Mohammad
Guillas, Serge
Probabilistic quantification of tsunami current hazard using statistical emulation
title Probabilistic quantification of tsunami current hazard using statistical emulation
title_full Probabilistic quantification of tsunami current hazard using statistical emulation
title_fullStr Probabilistic quantification of tsunami current hazard using statistical emulation
title_full_unstemmed Probabilistic quantification of tsunami current hazard using statistical emulation
title_short Probabilistic quantification of tsunami current hazard using statistical emulation
title_sort probabilistic quantification of tsunami current hazard using statistical emulation
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364761/
https://www.ncbi.nlm.nih.gov/pubmed/35153568
http://dx.doi.org/10.1098/rspa.2021.0180
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