Multiple Noise Reduction for Distributed Acoustic Sensing Data Processing through Densely Connected Residual Convolutional Networks

Multiple Noise Reduction for Distributed Acoustic Sensing Data Processing through Densely Connected Residual Convolutional Networks | 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 Multiple Noise Reduction for Distributed Acoustic Sensing Data Processing through Densely Connected Residual Convolutional Networks Tianye Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4023263/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2024 Read the published version in Journal of Applied Geophysics → Version 1 posted You are reading this latest preprint version Abstract Distributed acoustic sensing (DAS), which utilizes the entire optical fiber as the sensing medium, provides distinct advantages of high resolution, dynamic monitoring, and resistance to high temperatures. This technology finds diverse applications in the seismic exploration, oil survey, and submarine cable monitoring industries. However, DAS signals are susceptible to various kinds of noise, such as horizontal noise, optical noise, random noise, and so on, which significantly degrade the signal-to-noise ratio (SNR), this low SNR is likely to affect some subsequent analyses, such as inversion and interpretation. These mixed noises can pose a serious challenge to noise reduction in the DAS signal. To address this issue, we have developed a supervised learning-based densely connected residual convolutional denoising network (DCRCDNet), which leverages both encoding and decoding processes to extract features and reconstruct DAS data. The encoding and decoding processes enable the network to fully extract the number of features. The design of dense connectivity and residual blocks allow the network to better extract shallow to deep features, which ultimately reconstruct our DAS signal hidden in the noise. In comparison to the traditional filtering method and other deep learning methods, DCRCDNet has great potential for attenuating strong and mixed noise and extracting hidden signals. Distributed acoustic sensing seismic survey supervised learning encoding and decoding Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2024 Read the published version in Journal of Applied Geophysics → 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. 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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-4023263","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276917246,"identity":"98329d30-b80b-4e11-97d7-d09e3473e1ef","order_by":0,"name":"Tianye Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACZgiVACY/GNjYEdTBg6yFcUZBWjJhLQxIWph5PhxibCCkxZ6d+dljngqGPIPjZw+/tjE4wMzAfvjoBvwOYzM35jnDUGxwJi/NOsfgDh8DT1raDfxaGMykedsYEjccyDEzzjF4xswgwWNGQAv7N2nef0At59+YGVsYHGZsIKyFB2hLA1DLjRzjxwxEaTnMUyY55xhD4swbb8wYewzSktkI+YW9//g2iTc1DIl953OMP/z4Y2PHz374GF4tIMDEw/AfRLNJgElCykGA8QeEZv5AjOpRMApGwSgYeQAAcrJFWbu9lL0AAAAASUVORK5CYII=","orcid":"","institution":"School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan)","correspondingAuthor":true,"prefix":"","firstName":"Tianye","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-03-07 07:10:04","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4023263/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4023263/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.jappgeo.2024.105464","type":"published","date":"2024-07-01T22:45:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60626444,"identity":"6c7134bb-ffa8-4a30-aefc-8f51a6dfdb06","added_by":"auto","created_at":"2024-07-18 22:45:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1644682,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedmanuscriptfilewithoutChangesMarked.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4023263/v1_covered_a665d5d5-0032-474a-b291-d15e165368a1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMultiple Noise Reduction for Distributed Acoustic Sensing Data Processing through Densely Connected Residual Convolutional Networks\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan)","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Distributed acoustic sensing, seismic survey, supervised learning, encoding and decoding","lastPublishedDoi":"10.21203/rs.3.rs-4023263/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4023263/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDistributed acoustic sensing (DAS), which utilizes the entire optical fiber as the sensing medium, provides distinct advantages of high resolution, dynamic monitoring, and resistance to high temperatures. This technology finds diverse applications in the seismic exploration, oil survey, and submarine cable monitoring industries. However, DAS signals are susceptible to various kinds of noise, such as horizontal noise, optical noise, random noise, and so on, which significantly degrade the signal-to-noise ratio (SNR), this low SNR is likely to affect some subsequent analyses, such as inversion and interpretation. These mixed noises can pose a serious challenge to noise reduction in the DAS signal. To address this issue, we have developed a supervised learning-based densely connected residual convolutional denoising network (DCRCDNet), which leverages both encoding and decoding processes to extract features and reconstruct DAS data. The encoding and decoding processes enable the network to fully extract the number of features. The design of dense connectivity and residual blocks allow the network to better extract shallow to deep features, which ultimately reconstruct our DAS signal hidden in the noise. In comparison to the traditional filtering method and other deep learning methods, DCRCDNet has great potential for attenuating strong and mixed noise and extracting hidden signals.\u003c/p\u003e","manuscriptTitle":"Multiple Noise Reduction for Distributed Acoustic Sensing Data Processing through Densely Connected Residual Convolutional Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-08 07:48:54","doi":"10.21203/rs.3.rs-4023263/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f88ab4fb-f57d-4bef-89d2-dc55a87f79bf","owner":[],"postedDate":"March 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-18T22:45:19+00:00","versionOfRecord":{"articleIdentity":"rs-4023263","link":"https://doi.org/10.1016/j.jappgeo.2024.105464","journal":{"identity":"journal-of-applied-geophysics","isVorOnly":true,"title":"Journal of Applied Geophysics"},"publishedOn":"2024-07-01 22:45:19","publishedOnDateReadable":"July 1st, 2024"},"versionCreatedAt":"2024-03-08 07:48:54","video":"","vorDoi":"10.1016/j.jappgeo.2024.105464","vorDoiUrl":"https://doi.org/10.1016/j.jappgeo.2024.105464","workflowStages":[]},"version":"v1","identity":"rs-4023263","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4023263","identity":"rs-4023263","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}