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Real-Time Prediction of Alongshore Near-Field Tsunami Runup Distribution From Heterogeneous Earthquake Slip Distribution

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dc.contributor.authorLee, Jun-Whanen
dc.contributor.authorIrish, Jennifer L.en
dc.contributor.authorWeiss, Roberten
dc.date.accessioned2024-02-08T14:06:38Zen
dc.date.available2024-02-08T14:06:38Zen
dc.date.issued2023-01-05en
dc.description.abstractReal-time tsunami prediction is necessary for tsunami forecasting. Although tsunami forecasting based on a precomputed tsunami simulation database is fast, it is difficult to respond to earthquakes that are not in the database. As the computation speed increases, various alternatives based on physics-based models have been proposed. However, physics-based models still require several minutes to simulate tsunamis and can have numerical stability issues that potentially make them unreliable for use in forecasting—particularly in the case of near-field tsunamis. This paper presents a data-driven model called the tsunami runup response function for finite faults (TRRF-FF) model that can predict alongshore near-field tsunami runup distribution from heterogeneous earthquake slip distribution in less than a second. Once the TRRF-FF model is trained and calibrated based on a discrete set of tsunami simulations, the TRRF-FF model can predict alongshore tsunami runup distribution from any combination of finite fault parameters. The TRRF-FF model treats the leading-order contribution and the residual part of the alongshore tsunami runup distribution separately. The interaction between finite faults is modeled based on the leading-order alongshore tsunami runup distribution. We validated the TRRF-FF modeling approach with more than 200 synthetic tsunami scenarios in eastern Japan. We further explored the performance of the TRRF-FF model by applying it to the 2011 Tohoku (Japan) tsunami event. The results show that the TRRF-FF model is more flexible, occupies much less storage space than a precomputed tsunami simulation database, and is more rapid and reliable than real-time physics-based numerical simulation.en
dc.description.versionAccepted versionen
dc.format.extent21 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN e2022JC018873 (Article number)en
dc.identifier.doihttps://doi.org/10.1029/2022JC018873en
dc.identifier.eissn2169-9291en
dc.identifier.issn2169-9275en
dc.identifier.issue1en
dc.identifier.orcidWeiss, Robert [0000-0002-7168-5401]en
dc.identifier.orcidIrish, Jennifer [0000-0002-2429-5953]en
dc.identifier.urihttps://hdl.handle.net/10919/117896en
dc.identifier.volume128en
dc.language.isoenen
dc.publisherAmerican Geophysical Unionen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjecttsunamien
dc.subjectearthquakeen
dc.subjectforecastingen
dc.subjectrunupen
dc.subjectdata-driven modelingen
dc.titleReal-Time Prediction of Alongshore Near-Field Tsunami Runup Distribution From Heterogeneous Earthquake Slip Distributionen
dc.title.serialJournal of Geophysical Research-Oceansen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/Geosciencesen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Civil & Environmental Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen
pubs.organisational-group/Virginia Tech/Report testen

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