Global machine-learning detection of submarine calderas

Global machine-learning detection of submarine calderas | 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 Article Global machine-learning detection of submarine calderas Andrea Verolino, Christopher Lee, Susanna Jenkins, Martin Jutzeler, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7313707/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Submarine calderas are among the least documented volcanic structures on Earth, yet recent impactful events highlight their potential for severe geohazard consequences globally, including tsunamis, seafloor damage, and atmospheric impacts. Logistical limits have historically hindered global detection. Here we apply a machine-learning-based caldera detection algorithm to global bathymetry, enabling systematic identification of previously undetected submarine calderas. We found 78 calderas spanning different water depths (up to 5,600 m), diameters (up to 20 km) and tectonic settings (divergent, convergent, intraplate). A subset of eight shallow-water calderas, primarily within volcanic arcs, are identified as high-priority targets due to their high hazard potential. Our dataset fills a critical observational gap and offers a reproducible and upgradeable framework for submarine volcano characterization, geohazard assessment, and deep-sea exploration. These findings underscore the need to integrate submarine calderas into future global hazard modelling and monitoring strategies. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Solid Earth sciences/Volcanology Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SF1Featuresdataset.xlsx CDA output filtered list (all features detected) SF2Calderasdataset.xlsx CDA output list (only calderas and craters) SF3Featuresprofiles.pdf Topographic profiles for all filtered feature Cite Share Download PDF Status: Under Review 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. 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