Ground Motion Field Prediction Using a U-net Neural Network

Ground Motion Field Prediction Using a U-net Neural Network | 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 Article Ground Motion Field Prediction Using a U-net Neural Network Yujie Zhang, Jia Yi, Yushan Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6228720/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Ground motion field prediction is indispensable for seismic hazard assessment. This study proposes a ground motion field prediction method utilizing a U-Net neural network, whose goals are improved accuracy of ground motion prediction and effectiveness of strong ground motion numerical simulations. A large number of strong ground motion simulations were carried out and their peak ground motion accelerations (PGAs) were used for machine learning training. The results show that the U-Net neural network constructed in this paper can achieve a good PGA prediction. The proposed method only needs input from an observation station network and can then efficiently predict the ground motion field, which will be useful in practical applications. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Solid earth sciences Physical sciences/Engineering Neural network Ground motion field prediction PGA Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6228720","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":435847191,"identity":"eb4e09c5-e372-46cf-9bd9-b814f4d7cd04","order_by":0,"name":"Yujie Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYHCChAMQOvnAgQ8VxGsxANJpiQdnnCHeJpCWHOPDvC3EqL2R8PDAh5o/9vLtOR8O8DYwyPOLHSCoJeHgjGMGzAZn3m44ILmDwXDm7ATCWg7zsBmwGUjkbjhgeIYhweA2MVr+/DPgkZ+R8+BAYhuxWhjbDCQYbuQwHDhIjBbJMw8SDvb2GRsYnHlmcLDhjARhv/Adz0n+8OObHDDEkh9//lNhI88vTUCLwgEeFBUS+JWDgHwD+wHCqkbBKBgFo2BkAwACulB+3gJYIAAAAABJRU5ErkJggg==","orcid":"","institution":"China Earthquake Disaster Prevention Center","correspondingAuthor":true,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Zhang","suffix":""},{"id":435847192,"identity":"bce0dbf6-85c7-4a88-9f1b-4ec9b023e009","order_by":1,"name":"Jia Yi","email":"","orcid":"","institution":"China Earthquake Disaster Prevention Center","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Yi","suffix":""},{"id":435847193,"identity":"4ff1af46-311b-4d61-a50e-969933ead67d","order_by":2,"name":"Yushan Zhang","email":"","orcid":"","institution":"China Earthquake Disaster Prevention Center","correspondingAuthor":false,"prefix":"","firstName":"Yushan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-03-14 18:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6228720/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6228720/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96278909,"identity":"311b5655-3827-4a2d-98ba-77a3607403e8","added_by":"auto","created_at":"2025-11-19 10:39:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1251988,"visible":true,"origin":"","legend":"","description":"","filename":"GROUNDMOTIONFIELDPREDICTIONUSINGAUNETNEURALNETWORK.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6228720/v1_covered_5b403032-827c-4281-be1b-9ed768560c6d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGround Motion Field Prediction Using a U-net Neural Network\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neural network, Ground motion field prediction, PGA","lastPublishedDoi":"10.21203/rs.3.rs-6228720/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6228720/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGround motion field prediction is indispensable for seismic hazard assessment. 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