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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2021
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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. |
format | Online Article Text |
id | pubmed-8364761 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
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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