Improved Automatic Seismic Bulletins Via Likelihood-Based Model Fit Scores for Classification

Improved Automatic Seismic Bulletins Via Likelihood-Based Model Fit Scores for Classification | 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 Improved Automatic Seismic Bulletins Via Likelihood-Based Model Fit Scores for Classification Shahar Cohen, David M. Steinberg, Yael Radzyner, Yochai Ben Horin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9280795/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 Automatically produced seismic bulletins, such as the Standard Event List (SEL1) of the International Data Centre (IDC), contain many malformed events. Analysts frequently reject, split, or substantially modify events before they enter refined lists such as the Late Event Bulletin (LEB). A key weakness is that standard pipelines rely almost exclusively on positive detections and ignore non-detecting stations, even though a station that should have detected an event but did not provides strong evidence against that event’s legitimacy. We address this by creating an event scoring function that incorporates informative missing data from non-detections and other prior seismic knowledge. Using LEB as a training reference, we model for each station the detection probability and distribution of key observed summaries. For every SEL1 event, we compute likelihood-based model-fit score features that quantify how well the candidate event explains the observed detection pattern across the network. These physics-driven features feed a simple classifier that outputs a legitimacy score for each event. Applied to one year of independent SEL1 test data, the classifier identifies more than 72% of false events while falsely flagging only 5% of valid ones. Low-scoring events retained by analysts often correspond to cases where the score helps diagnose and correct data problems. The transparency and interpretability of the classifier make it well-suited to seismic monitoring, potentially as an analyst support tool, while preserving the benefits of a high-performing machine-learning model. Seismic monitoring Informative missingness Missing not at random Interpretable machine learning CTBTO IDC Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 May, 2026 Reviews received at journal 06 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 31 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. 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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-9280795","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622117175,"identity":"548c4183-00e6-4b06-861e-883d7a590b42","order_by":0,"name":"Shahar Cohen","email":"data:image/png;base64,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","orcid":"","institution":"Tel Aviv University","correspondingAuthor":true,"prefix":"","firstName":"Shahar","middleName":"","lastName":"Cohen","suffix":""},{"id":622117176,"identity":"76f6a2bb-0431-46f7-9054-34863665e95c","order_by":1,"name":"David M. 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Analysts frequently reject, split, or substantially modify events before they enter refined lists such as the Late Event Bulletin (LEB). A key weakness is that standard pipelines rely almost exclusively on positive detections and ignore non-detecting stations, even though a station that should have detected an event but did not provides strong evidence against that event’s legitimacy. We address this by creating an event scoring function that incorporates informative missing data from non-detections and other prior seismic knowledge. Using LEB as a training reference, we model for each station the detection probability and distribution of key observed summaries. For every SEL1 event, we compute likelihood-based model-fit score features that quantify how well the candidate event explains the observed detection pattern across the network. These physics-driven features feed a simple classifier that outputs a legitimacy score for each event. 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