Deep Learning Framework for Automatic Detection of Nano-Seismic Events

Deep Learning Framework for Automatic Detection of Nano-Seismic Events | 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 Deep Learning Framework for Automatic Detection of Nano-Seismic Events Ghada ali, Ali Gamal Hafez, Sayed Hasaneen, Hamed Nofel, Omar Saad, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9063822/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Identifying micro- and nanoseismic events is a crucial step in processing and analyzing hydraulic fracturing data. The low signal-to-noise ratios (S/N) of nanoseismic data render conventional event-detection techniques ineffective. Traditional techniques for detecting nano-seismic events rely on variations in the effective signal's amplitude, frequency, and correlation with background noise. For automatically detecting seismic events and identifying low-magnitude events with a magnitude range of -1.8 to 3.8, a Recurrent Convolutional Neural Network (RCNN) has been created based on an event detection system. To verify the feasibility of the approach, 20416 events and random noise records have been manually identified along with the relevant labels. The training results demonstrated the effectiveness of the method for event detection. The supplied data frame contains just 320 samples. We separate the seismic data into two groups: the event and the background noise, with corresponding values of 12332 and 8084. After training, the network architecture preserves the model parameters with the highest training accuracy and learns to compute the necessary attributes for event detection automatically. Physical sciences/Engineering Physical sciences/Mathematics and computing Earth and environmental sciences/Solid earth sciences Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 06 May, 2026 Reviews received at journal 04 May, 2026 Reviews received at journal 03 May, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 21 Apr, 2026 Editor invited by journal 21 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 02 Apr, 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. 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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-9063822","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":628545791,"identity":"9dc01f81-f44f-4551-96fa-51684c861d5c","order_by":0,"name":"Ghada ali","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACCTBikGFjb2BgSAALMTYQpYWHjecAqVoYJBKIdJd8dPPDGz8Y7Hj4JN8e+/CgppZBvv1wA8OHX7i1GN45ZmzZw5DMwyadlzwj4dhxBoMziQ2MM/vwaJmRYCbBw8AM1JJjzJDAdozBQIKxgZm3B5+W9G+SfxjqedgkzwC1/DvGID8DqOUvHi3yEjlm0jwMh3nYJHiMGRLbahgYbgC1MPzArcVAIqfYWobhODCQgQ5L7DvAA/LLwd4GPLbMSN948w1DtZx8+xljxh/f6oCM4w8f/PiDx5YDQILxH5x/mAdEHmBsw2MLmgvqoDQeW0bBKBgFo2DEAQBvPEsZZDKduwAAAABJRU5ErkJggg==","orcid":"","institution":"National Research Institute of Astronomy and Geophysics","correspondingAuthor":true,"prefix":"","firstName":"Ghada","middleName":"","lastName":"ali","suffix":""},{"id":628545792,"identity":"cce35436-66a1-4af6-a57f-bc05940f206e","order_by":1,"name":"Ali Gamal Hafez","email":"","orcid":"","institution":"National Research Institute of Astronomy and Geophysics","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Gamal","lastName":"Hafez","suffix":""},{"id":628545793,"identity":"79a59be7-e187-4fac-9240-1d454cb9989f","order_by":2,"name":"Sayed Hasaneen","email":"","orcid":"","institution":"Aswan University","correspondingAuthor":false,"prefix":"","firstName":"Sayed","middleName":"","lastName":"Hasaneen","suffix":""},{"id":628545794,"identity":"72fa90a8-8904-4162-850a-9bf5e4e4e586","order_by":3,"name":"Hamed Nofel","email":"","orcid":"","institution":"National Research Institute of Astronomy and Geophysics","correspondingAuthor":false,"prefix":"","firstName":"Hamed","middleName":"","lastName":"Nofel","suffix":""},{"id":628545795,"identity":"cc590372-39c3-4b22-b554-463d1ca89b24","order_by":4,"name":"Omar Saad","email":"","orcid":"","institution":"National Research Institute of Astronomy and Geophysics","correspondingAuthor":false,"prefix":"","firstName":"Omar","middleName":"","lastName":"Saad","suffix":""},{"id":628545796,"identity":"fb94836b-3749-496a-8b17-dbb2e210cb32","order_by":5,"name":"Ahmed Mohamed","email":"","orcid":"","institution":"Aswan University","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Mohamed","suffix":""}],"badges":[],"createdAt":"2026-03-08 11:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9063822/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9063822/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107870427,"identity":"3609995d-2aca-4c93-b9f0-7a22a2137fc9","added_by":"auto","created_at":"2026-04-27 07:39:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1431721,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9063822/v1_covered_1e48c6cc-7ece-4416-b30a-66ce5e0b2afb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning Framework for Automatic Detection of Nano-Seismic Events","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9063822/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9063822/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Identifying micro- and nanoseismic events is a crucial step in processing and analyzing hydraulic fracturing data. 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