Effects of Network-Specific Training and Waveform Denoising on ML-Based Seismic Phase Picking

Effects of Network-Specific Training and Waveform Denoising on ML-Based Seismic Phase Picking | 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 Effects of Network-Specific Training and Waveform Denoising on ML-Based Seismic Phase Picking Andreas Steinberg, Peter Gaebler, Catalina Ramos Domke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9268145/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract We train and evaluate the performance of deep learning based models for picking seismic phases from local, regional and teleseismic seismological datasets. The datasets for training and testing at regional and teleseismic distances are assembled based on bulletins provided by the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). In this study we test the potential of machine learning (ML) algorithms, namely phase picking and denoising, to potentially help with the detection and characterization of potential nuclear explosions. ML algorithms offer the chance of potentially faster operational procedures for the detection of explosive and non-explosive events at lower signal-to-noise ratios as currently. For training and testing on local distances a dataset provided by the German Federal Seismological Survey (GR) is used. The established phase picking algorithms PhaseNet and EQT are employed here in both original model form and with newly trained models. The network specific trained phase picker models are evaluated against other available models. We also test the influence that autoencoder based denoising has on the performance of deep learning based seismic phase picking. We train phase picking models on denoised data. We find that original models already generalize well on IMS and GR data. However training of network specific models improves predictions for teleseismic and regional distances. Application on denoised seismic waveforms necessitates dedicated models trained on denoised data. Seismology Machine Learning Deep Learning Software Comprehensive Nuclear-Test-Ban Treaty Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 30 Mar, 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. 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-9268145","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615970471,"identity":"ffba020d-22ba-48b0-91b5-2d4a0ecf4816","order_by":0,"name":"Andreas Steinberg","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYHACAzjrAGMDkGTvAbGZidDCBtPCc4YELQxgLRI5+LXwz27e9uFDTR2D/Pzmg4cLd9jky0e+Pfy6gsFaDpcWiTvHimfOOHaYweAYW8LhmWfSLDfezkuzPMOQbozTmhs5xsw8bAcYDNh4DA7zth02MJydY2bYwHA4sQGHDnmQlj//gA5r4/8A1PLfwHDmGbCWelxaDEBaGNuAnj3GwwDUcsBAXoLH+CFQSwIudxneSCtm7O07zGNwLA3osDPJBgY8eWmMDQbphrhskbuRvJnhx7c6Ofnmw48/8+6wM5BvP3v4Y0OFtTxO70MBD8KpBxjYJJCSBBFAvoGB+QMpGkbBKBgFo2DYAwAtzFgehP7iuwAAAABJRU5ErkJggg==","orcid":"","institution":"Federal Institute for Geosciences and Natural Resources","correspondingAuthor":true,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Steinberg","suffix":""},{"id":615970472,"identity":"d288c765-85ad-434d-b1a3-d87b0cce35ba","order_by":1,"name":"Peter Gaebler","email":"","orcid":"","institution":"Federal Institute for Geosciences and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Gaebler","suffix":""},{"id":615970473,"identity":"89100a76-d3cb-4fa4-abd1-672630f3f849","order_by":2,"name":"Catalina Ramos Domke","email":"","orcid":"","institution":"Federal Institute for Geosciences and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Catalina","middleName":"Ramos","lastName":"Domke","suffix":""}],"badges":[],"createdAt":"2026-03-30 14:10:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9268145/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9268145/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106404247,"identity":"054b8f4a-9e06-4c37-84ee-d440e0dba7a1","added_by":"auto","created_at":"2026-04-08 09:15:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6859813,"visible":true,"origin":"","legend":"","description":"","filename":"CTBTspecialissuePageophSteinbergetal20261.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9268145/v1_covered_315bb329-2a98-409b-9100-5319770fe5ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of Network-Specific Training and Waveform Denoising on ML-Based Seismic Phase Picking","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":"pure-and-applied-geophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"paag","sideBox":"Learn more about [Pure and Applied Geophysics](https://www.springer.com/journal/24)","snPcode":"24","submissionUrl":"https://submission.nature.com/new-submission/24/3","title":"Pure and Applied Geophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Seismology, Machine Learning, Deep Learning, Software, Comprehensive Nuclear-Test-Ban Treaty","lastPublishedDoi":"10.21203/rs.3.rs-9268145/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9268145/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe train and evaluate the performance of deep learning based models for picking seismic phases from local, regional and teleseismic seismological datasets. The datasets for training and testing at regional and teleseismic distances are assembled based on bulletins provided by the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). In this study we test the potential of machine learning (ML) algorithms, namely phase picking and denoising, to potentially help with the detection and characterization of potential nuclear explosions. ML algorithms offer the chance of potentially faster operational procedures for the detection of explosive and non-explosive events at lower signal-to-noise ratios as currently. For training and testing on local distances a dataset provided by the German Federal Seismological Survey (GR) is used. The established phase picking algorithms PhaseNet and EQT are employed here in both original model form and with newly trained models. The network specific trained phase picker models are evaluated against other available models. We also test the influence that autoencoder based denoising has on the performance of deep learning based seismic phase picking. We train phase picking models on denoised data. We find that original models already generalize well on IMS and GR data. However training of network specific models improves predictions for teleseismic and regional distances. Application on denoised seismic waveforms necessitates dedicated models trained on denoised data.\u003c/p\u003e","manuscriptTitle":"Effects of Network-Specific Training and Waveform Denoising on ML-Based Seismic Phase Picking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 21:56:26","doi":"10.21203/rs.3.rs-9268145/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T10:06:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T01:15:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T06:14:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257821473316184729182430140032846555326","date":"2026-04-17T18:17:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285866815098442952733364649703760397341","date":"2026-04-05T23:53:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-01T13:19:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-01T08:47:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T12:57:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pure and Applied Geophysics","date":"2026-03-30T14:00:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pure-and-applied-geophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"paag","sideBox":"Learn more about [Pure and Applied Geophysics](https://www.springer.com/journal/24)","snPcode":"24","submissionUrl":"https://submission.nature.com/new-submission/24/3","title":"Pure and Applied Geophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"04ddadd4-9392-4eb9-9276-41b38710a043","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T10:06:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T01:15:54+00:00","index":14,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T14:08:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 21:56:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9268145","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9268145","identity":"rs-9268145","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}