SegPhase: Development of Arrival Time Picking Models for Japan’s Seismic Network Using the Hierarchical Vision Transformer

SegPhase: Development of Arrival Time Picking Models for Japan’s Seismic Network Using the Hierarchical Vision Transformer | 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 SegPhase: Development of Arrival Time Picking Models for Japan’s Seismic Network Using the Hierarchical Vision Transformer Shinya Katoh, Yoshihisa Iio, Hiromichi Nagao, Hiroshi Katao, Masayo Sawada, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4291782/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jul, 2025 Read the published version in Earth, Planets and Space → Version 2 posted 5 You are reading this latest preprint version Show more versions Abstract We present SegPhase, a novel seismic arrival time picking model designed to efficiently process large-scale seismic data recorded by Japan’s dense seismic networks. SegPhase is implemented in three configurations to accommodate various observation settings. In contrast to conventional convolution-based models, SegPhase employs a hierarchical Vision Transformer structure that utilizes multi-head self-attention to dynamically focus on important waveform features, such as P- and S-wave onsets, noise, and coda waves. SegPhase improved the match rate of arrival time picks with human analysts by approximately 11% in the test dataset. Additionally, when applied to continuous waveforms, it detected approximately 15% more seismic events than the most commonly used deep learning model, PhaseNet, particularly enhancing the detection of small events (M0.6 or less). Benchmark evaluations demonstrated that SegPhase achieved high classification performance, almost 100% distinguishing between seismic signals and noise and accurately identifying different seismic wave phases. Additionally, arrival time picking exhibited low residuals, with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of 0.03 and 0.06 for P-waves and 0.06 and 0.10 for S-waves, respectively. We also examined the threshold of output probability values when applying SegPhase to continuous waveforms for which the optimal threshold is not known. By lowering the threshold to 0.1, we observed an increase in detected events without significant changes in hypocenter location error and observed-calculated (O-C) discrepancies. Furthermore, arrival times with high output probability were more effectively utilized. Based on these results, we recommend setting the threshold to 0.1 to enhance event detection while maintaining robust arrival time accuracy. Our findings demonstrate that SegPhase effectively picks seismic arrivals across diverse datasets, enhances event detection, and supports high-resolution seismic monitoring. Full Text Supplementary Files GraphicalAbstract17Apr2024.png submitpapersupplementaryEPS20250201.docx Cite Share Download PDF Status: Published Journal Publication published 21 Jul, 2025 Read the published version in Earth, Planets and Space → Version 2 posted Reviewers agreed at journal 09 Feb, 2025 Reviewers invited by journal 09 Feb, 2025 Editor assigned by journal 02 Feb, 2025 First submitted to journal 01 Feb, 2025 Editorial decision: Major Revision 28 May, 2024 You are reading this latest preprint version Show more versions 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. 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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-4291782","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":413148273,"identity":"950cffe8-1559-458d-9334-1408af93b7ef","order_by":0,"name":"Shinya 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