Physically driven feature engineering for deep learning applications in seismo-volcanic signal analysis

Physically driven feature engineering for deep learning applications in seismo-volcanic signal analysis | 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 Physically driven feature engineering for deep learning applications in seismo-volcanic signal analysis Kevin A. Vargas-Zamudio, Mathieu Perton, Denis Legrand, Francisco J. Sánchez-Sesma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6999643/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Dec, 2025 Read the published version in Earth Science Informatics → Version 1 posted 11 You are reading this latest preprint version Abstract The progressive growth of seismological databases has motivated the exploration of novel methodologies for common tasks such as detection and phase-picking, with a focus on maintaining reliability comparable to human performance. This goal consistently involves leveraging deep learning techniques, which emulate sensory processing in the human brain through numerical simulations. This study introduces a physically driven feature engineering approach that capitalizes on the inherent information within seismic data. While many contemporary studies train their models via robust raw datasets, practical alternatives tailored for smaller databases are often overlooked. Feature engineering in seismological contexts aims to develop deep learning models with tangible physical significance, specifically those that target event detection and phase-picking tasks across both local and regional seismic environments. Our approach involves physically driven transformations that incorporate amplitude spectra from signals filtered at predefined frequency bands, as well as spatial features such as angles computed from signal components like wave incidence and azimuth. This methodology is particularly pertinent in seismo-volcanic contexts, where accurate discrimination and characterization of seismic signals are pivotal for monitoring and risk assessment purposes. The incorporation of significant physical information from seismic signals into pattern recognition is crucial, as many feature engineering applications lack a contextual understanding of the data, which can lead to distortions, particularly within geophysical domains. Our results demonstrate human-level performance in these common tasks, harnessing the capabilities of statistical learning algorithms as a practical, resource-efficient solution for addressing these challenges on a large scale. Deep learning feature engineering seismic detection seismic phase-picking Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Dec, 2025 Read the published version in Earth Science Informatics → Version 1 posted Editorial decision: Revision requested 20 Oct, 2025 Reviews received at journal 18 Oct, 2025 Reviewers agreed at journal 02 Oct, 2025 Reviewers agreed at journal 27 Sep, 2025 Reviews received at journal 15 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers invited by journal 03 Aug, 2025 Editor assigned by journal 03 Aug, 2025 Submission checks completed at journal 14 Jul, 2025 First submitted to journal 28 Jun, 2025 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-6999643","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496251365,"identity":"2335bdde-f858-4caf-b03c-fff667aeb472","order_by":0,"name":"Kevin A. 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