SeisRWKV: Multi-scale Feature Interaction with Linear Complexity for Seismic Neighboring-shot Interference Mitigation

SeisRWKV: Multi-scale Feature Interaction with Linear Complexity for Seismic Neighboring-shot Interference Mitigation | 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 SeisRWKV: Multi-scale Feature Interaction with Linear Complexity for Seismic Neighboring-shot Interference Mitigation Yun Tang, Yuqing Wang, Xiaolin Wei, Xijun Feng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8709232/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 In seismic data acquisition, the independent simultaneous source method has emerged as a preferable alternative to traditionaltechniques, as it can improve acquisition efficiency while preserving data fidelity. However, the simultaneous activation ofmultiple seismic sources within a narrow time window often introduces neighboring-shot interference, which degrades thequality of acquired data—while seismic data processing is essentially a type of specialized image data processing task, it isfar more complex than natural image processing due to the non-stationary characteristics of seismic signals and the intricategeological backgrounds they reflect. In addition, the latency in single-shot data synthesis of autonomous acquisition nodesposes a major obstacle to supplementary data collection, further limiting the flexibility of subsequent data processing workflows.Traditional denoising methods are incompetent in addressing such interference, mainly due to two critical drawbacks: the highsimilarity between interfering signals and primary seismic signals, and their excessive reliance on manually tuned empiricalparameters. On the other hand, although deep learning has achieved remarkable success in natural image denoising, currentdeep learning-based denoising methods still face practical challenges when applied to seismic data processing scenarios:the scarcity of high-quality noisy-clean labeled sample pairs, the limited receptive field of convolutional neural networks thathinders long-range feature modeling, and the high computational complexity of Transformer models that prevents real-timedata processing. To overcome these issues, this study develops a specialized deep learning framework for neighboring-shotinterference removal and introduces the SeisRWKV model as its core component. The model employs a co-wkv bidirectionalattention mechanism to conduct global feature modeling with linear complexity, which efficiently reduces computational costswhile ensuring comprehensive sequence representation. Furthermore, the incorporation of a Multi-Channel Fusion (MCF)module enhances the fusion of information across different feature channels, strengthens the model’s ability to capture multi-scale features, and enables accurate extraction of contextual information for targeted interference suppression. Experimentsdemonstrate that SeisRWKV can effectively eliminate neighboring-shot interference, significantly improving the signal-to-noiseratio of seismic data, with performance surpassing other methods. Physical sciences/Engineering Physical sciences/Mathematics and computing Earth and environmental sciences/Solid earth sciences SeisRWKV neighboring-shot interference Deep Learning independent simultaneous source 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-8709232","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":586257670,"identity":"dfa1c3fb-e738-48b0-b065-3446a5862b6b","order_by":0,"name":"Yun Tang","email":"","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Tang","suffix":""},{"id":586257671,"identity":"490fbae8-36b0-44ce-987d-f7013d888613","order_by":1,"name":"Yuqing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYJACZhDBT7oWyQaStRgcIFa5wbXTiZ8Lau7YbT6evIHhR8U2IrTczt0sPePYs+RtZ54VMPacuU1Yi9nt3G3MPGyHk81u5BgwM7YRreXf4WTjGSRp4W07bGcgQawWe5BfePsOJ0gA/XKQKL9Izs7d+Jnn22F7/vbkjQ9+VBChBQYSGxgSiI8aiAMZgFpI0jEKRsEoGAUjBwAAuV4/HY/sMLoAAAAASUVORK5CYII=","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yuqing","middleName":"","lastName":"Wang","suffix":""},{"id":586257672,"identity":"f0ae6c32-5a32-45d3-ad74-18e487c93ff8","order_by":2,"name":"Xiaolin Wei","email":"","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaolin","middleName":"","lastName":"Wei","suffix":""},{"id":586257673,"identity":"64d6b2f8-0cf5-4d80-b285-39086f4a5c27","order_by":3,"name":"Xijun Feng","email":"","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xijun","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2026-01-27 10:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8709232/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8709232/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107705977,"identity":"8187caed-229b-49e4-9882-627749259848","added_by":"auto","created_at":"2026-04-24 09:16:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11931291,"visible":true,"origin":"","legend":"","description":"","filename":"sei.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8709232/v1_covered_94a4fbaf-de6c-4d7c-8956-c14cab0aa467.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SeisRWKV: Multi-scale Feature Interaction with Linear Complexity for Seismic Neighboring-shot Interference Mitigation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"SeisRWKV, neighboring-shot interference, Deep Learning, independent simultaneous source","lastPublishedDoi":"10.21203/rs.3.rs-8709232/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8709232/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn seismic data acquisition, the independent simultaneous source method has emerged as a preferable alternative to traditionaltechniques, as it can improve acquisition efficiency while preserving data fidelity. However, the simultaneous activation ofmultiple seismic sources within a narrow time window often introduces neighboring-shot interference, which degrades thequality of acquired data—while seismic data processing is essentially a type of specialized image data processing task, it isfar more complex than natural image processing due to the non-stationary characteristics of seismic signals and the intricategeological backgrounds they reflect. In addition, the latency in single-shot data synthesis of autonomous acquisition nodesposes a major obstacle to supplementary data collection, further limiting the flexibility of subsequent data processing workflows.Traditional denoising methods are incompetent in addressing such interference, mainly due to two critical drawbacks: the highsimilarity between interfering signals and primary seismic signals, and their excessive reliance on manually tuned empiricalparameters. On the other hand, although deep learning has achieved remarkable success in natural image denoising, currentdeep learning-based denoising methods still face practical challenges when applied to seismic data processing scenarios:the scarcity of high-quality noisy-clean labeled sample pairs, the limited receptive field of convolutional neural networks thathinders long-range feature modeling, and the high computational complexity of Transformer models that prevents real-timedata processing. To overcome these issues, this study develops a specialized deep learning framework for neighboring-shotinterference removal and introduces the SeisRWKV model as its core component. The model employs a co-wkv bidirectionalattention mechanism to conduct global feature modeling with linear complexity, which efficiently reduces computational costswhile ensuring comprehensive sequence representation. Furthermore, the incorporation of a Multi-Channel Fusion (MCF)module enhances the fusion of information across different feature channels, strengthens the model’s ability to capture multi-scale features, and enables accurate extraction of contextual information for targeted interference suppression. Experimentsdemonstrate that SeisRWKV can effectively eliminate neighboring-shot interference, significantly improving the signal-to-noiseratio of seismic data, with performance surpassing other methods.\u003c/p\u003e","manuscriptTitle":"SeisRWKV: Multi-scale Feature Interaction with Linear Complexity for Seismic Neighboring-shot Interference Mitigation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 06:06:43","doi":"10.21203/rs.3.rs-8709232/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":"e0230ae1-3e47-4379-be8a-0b50643a4870","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62375112,"name":"Physical sciences/Engineering"},{"id":62375113,"name":"Physical sciences/Mathematics and computing"},{"id":62375114,"name":"Earth and environmental sciences/Solid earth sciences"}],"tags":[],"updatedAt":"2026-04-22T08:12:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 06:06:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8709232","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8709232","identity":"rs-8709232","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}