Tsunami Height Estimation via Gaussian Process Regression Using Maximum Absolute Pressure Change and Time from Seafloor Sensors off the Kii Peninsula, Japan

Tsunami Height Estimation via Gaussian Process Regression Using Maximum Absolute Pressure Change and Time from Seafloor Sensors off the Kii Peninsula, Japan | 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 Tsunami Height Estimation via Gaussian Process Regression Using Maximum Absolute Pressure Change and Time from Seafloor Sensors off the Kii Peninsula, Japan Yutaro Iwabuchi, Toshitaka Baba, Takane Hori, Masato Okada, Yasuhiko Igarashi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5441059/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Aug, 2025 Read the published version in Marine Geophysical Research → Version 1 posted 12 You are reading this latest preprint version Abstract The Dense Ocean-floor Network for Earthquakes and Tsunamis (DONET) was recently installed to monitor tsunamis in the Nankai Trough. In this study, an advanced tsunami prediction model using Gaussian process regression that is suitable for seafloor pressure observations is proposed. In traditional approaches, only the maximum absolute pressure change recorded by seafloor pressure sensors was used as an explanatory variable. The proposed method includes the time when the maximum absolute pressure change is recorded as an explanatory variable. Because tsunami data observed by ocean observatories are insufficient for constructing Gaussian regression relationships, numerical tsunami simulations are used for learning and validation. After a tsunami is detected by DONET, the tsunami height prediction accuracy along the coast is increased by considering the time of the maximum absolute pressure change at seafloor pressure sensors. The proposed model enables rapid and effective estimation of coastal tsunami heights. Gaussian process regression Early tsunami prediction DONET High-precision tsunami simulation Seafloor pressure sensor data Time information Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Aug, 2025 Read the published version in Marine Geophysical Research → Version 1 posted Editorial decision: Revision requested 12 Mar, 2025 Reviews received at journal 10 Mar, 2025 Reviews received at journal 23 Feb, 2025 Reviewers agreed at journal 31 Jan, 2025 Reviewers agreed at journal 31 Jan, 2025 Reviewers agreed at journal 30 Jan, 2025 Reviewers agreed at journal 25 Jan, 2025 Reviewers agreed at journal 30 Dec, 2024 Reviewers invited by journal 20 Dec, 2024 Editor assigned by journal 13 Nov, 2024 Submission checks completed at journal 13 Nov, 2024 First submitted to journal 12 Nov, 2024 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. 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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-5441059","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377904099,"identity":"07cf630c-afba-45ec-8fda-71daef0bac13","order_by":0,"name":"Yutaro Iwabuchi","email":"","orcid":"","institution":"University of Tsukuba","correspondingAuthor":false,"prefix":"","firstName":"Yutaro","middleName":"","lastName":"Iwabuchi","suffix":""},{"id":377904100,"identity":"352bf5d1-3094-4ab3-bd41-8b6e8891c6b1","order_by":1,"name":"Toshitaka Baba","email":"","orcid":"","institution":"Tokushima 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