Seismic Phase Detection with Limited Data

Seismic Phase Detection with Limited Data | 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 Seismic Phase Detection with Limited Data Asif Ashraf, Mohammad Tawhidur Rahman Tushar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8781668/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Seismic phase picking is fundamental to earthquake detection and hazard assessment, yet modern deep learning approaches typically require hundreds of thousands of labeled seismograms—limiting their applicability in data-scarce regions. We present a U-Net-based framework that achieves high-accuracy P-wave arrival detection using only a few hundred training samples by integrating signal-derived features directly into the model input. These features, including multiscale STA/LTA ratios, Hilbert-envelope derivatives, frequency-band energy attributes, and maximum-amplitude volatility, provide the network with physically meaningful cues analogous to those used by human analysts. Trained on as few as 92 seismograms, the model achieves sub-second median pick errors (0.46–0.75 s) and robust convergence, while models trained solely on raw waveforms exhibit systematic biases exceeding 9 s. Feature-weight analysis reveals that envelope- and frequency-based features dominate model decision-making, confirming that incorporating domain knowledge guides learning toward interpretable, physically consistent representations. The approach dramatically reduces data requirements—by more than three orders of magnitude compared to state-of-the-art pickers such as PhaseNet or EQTransformer—while maintaining comparable accuracy. This signal-guided neural architecture bridges traditional seismological feature extraction with modern machine learning, providing a practical, data-efficient, and interpretable solution for automated phase picking in both well-instrumented and data-limited regions. Earthquake Detection Machine Learning Physics-Informed Neural Networks Seismology Small Datasets U-Net Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 14 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviews received at journal 23 Mar, 2026 Reviews received at journal 12 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 06 Feb, 2026 Submission checks completed at journal 06 Feb, 2026 First submitted to journal 03 Feb, 2026 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. 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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-8781668","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589042862,"identity":"8b93ef9a-c345-4825-948c-c7acce6b83d0","order_by":0,"name":"Asif Ashraf","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYHACNiCWk0MSSCBKi7Ex6VoSG4jWwt9+/NnjihqD9A3Hex8+/FJxh4GfPccArxaJMznmhmeOGeRuOHPc2FjmzDMGyZ43+LUwHMhhk2xg+5O74UYam7Rk22EGgxsEbJE///yZZMM/g3SDG2nsv0Fa7AlpMbiRYCbZ2GaQANTCxvgRZIsEAS2GN94AtfQZGM48c4xZmuHMYR6JM88K8GqRO58OdNg3A3m+422MH39UHJbjb0/egFcLCmDmYWDgIV45CDD+IE39KBgFo2AUjBAAAAY4SFzoVftqAAAAAElFTkSuQmCC","orcid":"","institution":"University of Oregon","correspondingAuthor":true,"prefix":"","firstName":"Asif","middleName":"","lastName":"Ashraf","suffix":""},{"id":589042863,"identity":"13762c46-d5a3-4206-a66a-8e0bcaf0ecad","order_by":1,"name":"Mohammad Tawhidur Rahman Tushar","email":"","orcid":"","institution":"University of Dhaka","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Tawhidur Rahman","lastName":"Tushar","suffix":""}],"badges":[],"createdAt":"2026-02-04 04:24:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8781668/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8781668/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102397535,"identity":"5759c491-0ba8-4245-8ceb-fe2b518c8a82","added_by":"auto","created_at":"2026-02-11 10:17:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15093062,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXsubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8781668/v1_covered_518605c1-a224-436c-ac2d-a763c0ae1956.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seismic Phase Detection with Limited Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-seismology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jose","sideBox":"Learn more about [Journal of Seismology](http://link.springer.com/journal/10950)","snPcode":"10950","submissionUrl":"https://submission.nature.com/new-submission/10950/3","title":"Journal of Seismology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Earthquake Detection, Machine Learning, Physics-Informed Neural Networks, Seismology, Small Datasets, U-Net","lastPublishedDoi":"10.21203/rs.3.rs-8781668/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8781668/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Seismic phase picking is fundamental to earthquake detection and hazard assessment, yet modern deep learning approaches typically require hundreds of thousands of labeled seismograms—limiting their applicability in data-scarce regions. 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