Deep learning prediction model for ground motion amplification effect of sedimentary valleys with varying shear wave velocities

Deep learning prediction model for ground motion amplification effect of sedimentary valleys with varying shear wave velocities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Deep learning prediction model for ground motion amplification effect of sedimentary valleys with varying shear wave velocities Jia-wei Zhao, Si-bo Meng, Zhong-xian Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7057392/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Apr, 2026 Read the published version in Pure and Applied Geophysics → Version 1 posted 10 You are reading this latest preprint version Abstract The site effects of sedimentary valleys caused by earthquakes were widely investigated using numerical methods, which had the challenge of the high computational costs. This study explores the feasibility of deep learning methods for obtaining the nonlinear seismic response of sedimentary valleys with varying shear wave velocities. The proposed deep learning model was constructed based on the Long Short-Term Memory (LSTM) network with the hybrid input features, including time series of input waves and shear wave velocities of valleys. The output features of this model were the time series of the seismic response at representative surface locations of sedimentary valleys, and the surface locations were determined via the Principal Component Analysis (PCA). Based on the model validation, the site effect and structural fragility solved by both the proposed LSTM model and traditional one-dimensional (1-D) soil analysis method were compared and discussed. The results indicate that the LSTM model exhibits great efficiency and precision (Coefficient of determination R 2 = 0.96) in assessing the site response and fragility analysis. Compared to LSTM models, the 1-D soil analysis method exhibits a notable underestimation of the structural fragility, with a maximum difference of approximately 50%. Sedimentary valley Deep learning Principal component analysis Site effect Seismic fragility Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Apr, 2026 Read the published version in Pure and Applied Geophysics → Version 1 posted Editorial decision: Revision requested 22 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviews received at journal 21 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviewers invited by journal 22 Aug, 2025 Editor assigned by journal 08 Jul, 2025 Submission checks completed at journal 07 Jul, 2025 First submitted to journal 06 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7057392","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506914229,"identity":"017b5bcb-7f44-457f-86e4-7adc896cdcdc","order_by":0,"name":"Jia-wei Zhao","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Jia-wei","middleName":"","lastName":"Zhao","suffix":""},{"id":506914230,"identity":"8bf97d21-5496-46f1-8f01-02c246d4338b","order_by":1,"name":"Si-bo Meng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACZh4IzQ9ik6ZFsgGqhYewHqgSgwPEapFv5z346EbFHbvNN5IPfi5guCNnT0iLwWG+ZOOcM8+St91IS5aewfDMmKAtBsw8ZtK5bYeTzW7kGEjzMBxO7CHosGaQln+Hk41n5H/+DdRST1ALw2GQlobDdgYSOWwgWxIIOwzsl2OHEyTOPDOz5jE4bNhzgJDD+s8efJxTc9ievz358W2eisPy7A0EXQYBiQ0CCSBLiVQOAvYM/IQcNApGwSgYBSMWAAB3PDzvs4DM+QAAAABJRU5ErkJggg==","orcid":"","institution":"Tianjin Chengjian University","correspondingAuthor":true,"prefix":"","firstName":"Si-bo","middleName":"","lastName":"Meng","suffix":""},{"id":506914231,"identity":"23794511-d7a0-4366-a688-fa7e6d53d8a7","order_by":2,"name":"Zhong-xian Liu","email":"","orcid":"","institution":"Tianjin Chengjian University","correspondingAuthor":false,"prefix":"","firstName":"Zhong-xian","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-07-06 11:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7057392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7057392/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00024-026-03979-0","type":"published","date":"2026-04-17T15:58:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":107350847,"identity":"f4182b72-8105-4a04-a217-1c796c2491e7","added_by":"auto","created_at":"2026-04-20 16:05:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2203435,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7057392/v1_covered_060721d3-4527-424a-b7f9-77f70fae87c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep learning prediction model for ground motion amplification effect of sedimentary valleys with varying shear wave velocities","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pure-and-applied-geophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"paag","sideBox":"Learn more about [Pure and Applied Geophysics](https://www.springer.com/journal/24)","snPcode":"24","submissionUrl":"https://submission.nature.com/new-submission/24/3","title":"Pure and Applied Geophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sedimentary valley, Deep learning, Principal component analysis, Site effect, Seismic fragility","lastPublishedDoi":"10.21203/rs.3.rs-7057392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7057392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe site effects of sedimentary valleys caused by earthquakes were widely investigated using numerical methods, which had the challenge of the high computational costs. This study explores the feasibility of deep learning methods for obtaining the nonlinear seismic response of sedimentary valleys with varying shear wave velocities. The proposed deep learning model was constructed based on the Long Short-Term Memory (LSTM) network with the hybrid input features, including time series of input waves and shear wave velocities of valleys. The output features of this model were the time series of the seismic response at representative surface locations of sedimentary valleys, and the surface locations were determined via the Principal Component Analysis (PCA). Based on the model validation, the site effect and structural fragility solved by both the proposed LSTM model and traditional one-dimensional (1-D) soil analysis method were compared and discussed. 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