A Knowledge-Driven Deep Learning System for Amplitude Window Selection in Seismic Signals

A Knowledge-Driven Deep Learning System for Amplitude Window Selection in Seismic Signals | 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 A Knowledge-Driven Deep Learning System for Amplitude Window Selection in Seismic Signals Ariana M. Villegas-Suarez, Delaine Reiter, Eli Baker, Evans Onyango, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8419706/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 To characterize global seismic activity accurately, monitoring systems require the onset times and amplitudes of specific seismic phase arrivals in observed waveform data. However, phase amplitudes remain difficult to measure automatically, with current methods achieving only 64% accuracy and leaving 36% of windows to manual correction by experts, creating a major real-time bottleneck in seismic monitoring workflows. To address this limitation, we introduce two knowledge-driven deep learning systems that learn expert decision-making patterns from analyst-corrected seismic data, thus improving automated amplitude measurement. Our approach leverages a U-Net architecture and a novel hybrid loss function that encodes expert preferences for pixel accuracy, shape regularity, and temporal consistency in amplitude window selection. We evaluated 80,648 seismic signal windows to assess the capabilities of our deep learning models. The models achieved substantially higher accuracy in amplitude measurement than traditional rule-based systems. When their outputs diverged from analyst-selected labels, we observed considerable variability among analysts, highlighting the subjective nature of the task. However, independent seismologists showed a strong preference for our model outputs. These findings suggest that our deep learning models successfully capture expert decision-making patterns, demonstrating effective knowledge transfer in an inherently subjective domain. Expert Knowledge Learning Time Series Deep Learning Transformers Seismic Signal Amplitude Window Full Text Additional Declarations No competing interests reported. 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-8419706","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":564714187,"identity":"6302dee0-87b8-4146-b32e-61ba2897ccca","order_by":0,"name":"Ariana M. 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