Fourier Neural Operator-Based Surrogate Modeling of Offshore Tsunami Propagation and Its Application to Rapid Tsunami Source Inversion

Fourier Neural Operator-Based Surrogate Modeling of Offshore Tsunami Propagation and Its Application to Rapid Tsunami Source Inversion | 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 Fourier Neural Operator-Based Surrogate Modeling of Offshore Tsunami Propagation and Its Application to Rapid Tsunami Source Inversion Masayoshi Someya, Takashi Furumura, Ryoichiro Agata This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9597846/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 The accurate and rapid estimation of tsunami sources immediately after an earthquake is essential for real-time tsunami forecasting. Recent advances in offshore tsunami observation networks have made it increasingly feasible to estimate tsunami sources from observed waveforms using adjoint-based inversion methods. However, conventional adjoint approaches require repeated forward and adjoint simulations, resulting in substantial computational cost that limit their applicability to real-time systems. To address this challenge, we introduce a surrogate model for tsunami propagation based on a Fourier Neural Operator (FNO). The proposed FNO model learns tsunami propagation governed by linear long-wave equations and efficiently approximates operator mapping from the initial conditions to future wavefields, enabling extremely fast tsunami simulations. Furthermore, by leveraging automatic differentiation, the model computes the gradients of the objective function efficiently without explicitly deriving the adjoint equations, thereby significantly simplifying the implementation of inversion methods. In this study, we develop an initial height inversion method to estimate the initial sea surface height and a fault parameter inversion framework by integrating an FNO model with a crustal deformation model. The proposed methods were validated using both synthetic data and real tsunami observations from the 2024 Hyuga-nada earthquake (M 7.1) recorded by the N-net system. The results demonstrate that accurate and rapid source estimation can be achieved within a few minutes, indicating the potential of the proposed approach as an efficient inversion framework for future real-time tsunami forecasting systems. Seismology Tsunami Inversion Neural Operator Scientific Machine Learning Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryInformation.pdf 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-9597846","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633529391,"identity":"24754992-a8a2-4376-9672-6e2d07752d22","order_by":0,"name":"Masayoshi 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