A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California

[EN] After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically limi...

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Autores: Monterrubio-Velasco, Marisol, Callaghan, Scott, Modesto, David, Carrasco, Jose Carlos, Badía, Rosa M., Vázquez-Novoa, Fernando, Pienkowska, Marta, de la Puente, Josep, Pallarés-Font de Mora, Pablo|||0000-0002-0120-3251, Quintana-Ortí, Enrique S.|||0000-0002-5454-165X
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/221212
Acceso en línea:https://riunet.upv.es/handle/10251/221212
Access Level:acceso abierto
Palabra clave:Large-magnitude earthquakes
Impact assessment
Ground shaking
Real-time constraints
Intensity measures
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network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California
title A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California
spellingShingle A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California
Monterrubio-Velasco, Marisol
Large-magnitude earthquakes
Impact assessment
Ground shaking
Real-time constraints
Intensity measures
title_short A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California
title_full A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California
title_fullStr A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California
title_full_unstemmed A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California
title_sort A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California
dc.creator.none.fl_str_mv Monterrubio-Velasco, Marisol
Callaghan, Scott
Modesto, David
Carrasco, Jose Carlos
Badía, Rosa M.
Vázquez-Novoa, Fernando
Pienkowska, Marta
de la Puente, Josep
Pallarés-Font de Mora, Pablo|||0000-0002-0120-3251
Quintana-Ortí, Enrique S.|||0000-0002-5454-165X
author Monterrubio-Velasco, Marisol
author_facet Monterrubio-Velasco, Marisol
Callaghan, Scott
Modesto, David
Carrasco, Jose Carlos
Badía, Rosa M.
Vázquez-Novoa, Fernando
Pienkowska, Marta
de la Puente, Josep
Pallarés-Font de Mora, Pablo|||0000-0002-0120-3251
Quintana-Ortí, Enrique S.|||0000-0002-5454-165X
author_role author
author2 Callaghan, Scott
Modesto, David
Carrasco, Jose Carlos
Badía, Rosa M.
Vázquez-Novoa, Fernando
Pienkowska, Marta
de la Puente, Josep
Pallarés-Font de Mora, Pablo|||0000-0002-0120-3251
Quintana-Ortí, Enrique S.|||0000-0002-5454-165X
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Informática de Sistemas y Computadores
Escuela Técnica Superior de Ingeniería Informática
Grupo de Arquitecturas Paralelas
European Commission
U.S. Department of Energy
Agencia Estatal de Investigación
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Large-magnitude earthquakes
Impact assessment
Ground shaking
Real-time constraints
Intensity measures
topic Large-magnitude earthquakes
Impact assessment
Ground shaking
Real-time constraints
Intensity measures
description [EN] After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically limit the accuracy of the estimated values. As an alternative, here we present Machine Learning strategies trained on physics-based simulations that require similar evaluation times. We trained and validated the proposed Machine Learning-based Estimator for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided that the events considered are compatible with the training data. Using the proposed strategy we show significant error reductions not only for synthetic, but also for five real historical earthquakes, relative to empirical Ground Motion Models.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-05-16
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/221212
url https://riunet.upv.es/handle/10251/221212
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PCI2021-121957 ENABLING DYNAMIC AND INTELLIGENT WORKFLOWS IN THE FUTURE EUROHPCECOSYSTEM
European Commission https://doi.org/10.13039/501100000780 H2020 955558 Enabling dynamic and Intelligent workflows in the future EuroHPCecosystem
European Commission https://doi.org/10.13039/501100000780 HE 101093038 Second Phase
U.S. Department of Energy https://doi.org/10.13039/100000015 DE-AC05-00OR22725
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern CaliforniaMonterrubio-Velasco, MarisolCallaghan, ScottModesto, DavidCarrasco, Jose CarlosBadía, Rosa M.Vázquez-Novoa, FernandoPienkowska, Martade la Puente, JosepPallarés-Font de Mora, Pablo|||0000-0002-0120-3251Quintana-Ortí, Enrique S.|||0000-0002-5454-165XLarge-magnitude earthquakesImpact assessmentGround shakingReal-time constraintsIntensity measures[EN] After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically limit the accuracy of the estimated values. As an alternative, here we present Machine Learning strategies trained on physics-based simulations that require similar evaluation times. We trained and validated the proposed Machine Learning-based Estimator for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided that the events considered are compatible with the training data. Using the proposed strategy we show significant error reductions not only for synthetic, but also for five real historical earthquakes, relative to empirical Ground Motion Models.This work has been funded by the European Commission's Horizon 2020 Framework program and the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955558 and by MCIN/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR (PCI2021-121957), project eFlows4HPC. This research has been supported by the European High-Performance Computing Joint Undertaking (JU) as well as Spain, Italy, Iceland, Germany, Norway, France, Finland, and Croatia under grant agreement no. 101093038, (ChEESE-CoE) The authors acknowledge the Center for Advanced Research Computing (CARC) at the University of Southern California for providing computing resources that have contributed to the research results reported within this publication. URL: https://carc.usc.edu. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725, and the National Center for Supercomputer Applications under the Blue Waters Sustained-Petascale Computing Project. SCEC is funded by USGS Cooperative Agreement G17AC00047 and NSF Cooperative Agreement EAR-1600087. The authors also thank Dr. Kevin Milner, who provided the ASK-14 estimations needed for the comparisons presented in this paper. The authors thank Dr. Arnau Folch who reviewed the draft of the paper, improving it with his comments and suggestions.Nature Publishing GroupDepartamento de Informática de Sistemas y ComputadoresEscuela Técnica Superior de Ingeniería InformáticaGrupo de Arquitecturas ParalelasEuropean CommissionU.S. Department of EnergyAgencia Estatal de InvestigaciónRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-05-16journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/221212reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PCI2021-121957 ENABLING DYNAMIC AND INTELLIGENT WORKFLOWS IN THE FUTURE EUROHPCECOSYSTEMEuropean Commission https://doi.org/10.13039/501100000780 H2020 955558 Enabling dynamic and Intelligent workflows in the future EuroHPCecosystemEuropean Commission https://doi.org/10.13039/501100000780 HE 101093038 Second PhaseU.S. Department of Energy https://doi.org/10.13039/100000015 DE-AC05-00OR22725open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2212122026-06-13T07:49:27Z
score 15,198674

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