Diffraction separation method using shapeDTW and median-mean filter

Diffraction separation method using shapeDTW and median-mean filter | 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 Diffraction separation method using shapeDTW and median-mean filter Tongjie Sheng, Jingtao Zhao, Jie Yang, Zongnan Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5162658/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jun, 2025 Read the published version in Acta Geophysica → Version 1 posted 6 You are reading this latest preprint version Abstract The subsurface small-scale geological structures are manifested as diffractions in seismic data. Diffraction imaging provides high-resolution details of discontinuities such as faults, collapse columns, and karst caves. However, this high-resolution information is often obfuscated by strong reflections, necessitating their removal prior to diffraction imaging. Here, we propose a novel diffraction separation method based on shape dynamic time warping (shapeDTW) and median-mean filter. The shapeDTW is an effective time series alignment method that utilizes the distance between temporal points within a neighborhood as the alignment criterion, which can accurately align strong energy events in seismic data. We implement shapeDTW to construct flattened reflection gathers, in which the reflections are aligned and therefore behaves as horizontal events with consistent strong amplitudes, while the diffractions appear as non-horizontal weak events. Leveraging this difference in shape and amplitude, the median-mean filter can effectively extract the reflections from flattened reflection gathers. Diffractions are separated from seismic data by subtracting the extracted reflections. The synthetic data experiment confirms the feasibility of the proposed method in eliminating strong reflected waves while preserving weak diffracted waves related to karst caves in seismic data with low signal-to-noise ratio. The field data application further illustrates its effectiveness in removing strong high-slope reflections, highlighting small-scale fracture-related detailed features. diffraction separation shapeDTW flattened reflection gathers median-mean filter Full Text Cite Share Download PDF Status: Published Journal Publication published 03 Jun, 2025 Read the published version in Acta Geophysica → Version 1 posted Editorial decision: Major revisions 27 Nov, 2024 Reviewers agreed at journal 11 Oct, 2024 Reviewers invited by journal 10 Oct, 2024 Editor invited by journal 09 Oct, 2024 Editor assigned by journal 03 Oct, 2024 First submitted to journal 30 Sep, 2024 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-5162658","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":364680687,"identity":"1888513f-4609-4e70-a1c1-9f10466eb962","order_by":0,"name":"Tongjie Sheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACNmbm4x8kKv7bybM3HyBOCx87WxqDxRnmZMOeYwnEaZHj5zFjqGxjZmy4kWNArMN4zB7cYGNjZuw58/HGGwY7Od0GglrYyg1n8PDwsbP3bracw5BsbHaAoBbmDdISEhJAW85uk+ZhOJC4jbAWBgPpPwYGIL88I1YLi5mEREICSAsbsVrYkg0kDhwABbKx5RwDIvwi33/44APJfwdAUfnwxpsKOzmCWlCABA+RUYOshVQdo2AUjIJRMCIAAISCPNvYxy4IAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0716-2067","institution":"China University of Mining and Technology - Beijing","correspondingAuthor":true,"prefix":"","firstName":"Tongjie","middleName":"","lastName":"Sheng","suffix":""},{"id":364680688,"identity":"f16ea272-3f76-4a6d-b153-3eb99f09e0a3","order_by":1,"name":"Jingtao Zhao","email":"","orcid":"","institution":"China University of Mining and Technology - Beijing","correspondingAuthor":false,"prefix":"","firstName":"Jingtao","middleName":"","lastName":"Zhao","suffix":""},{"id":364680689,"identity":"6e0fdfeb-eaff-4bc7-9704-a4615444ffec","order_by":2,"name":"Jie Yang","email":"","orcid":"","institution":"China University of Mining and Technology - Beijing","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Yang","suffix":""},{"id":364680690,"identity":"1350c97c-e9b6-4b88-aaa5-8807ab8ce615","order_by":3,"name":"Zongnan Chen","email":"","orcid":"","institution":"China University of Mining and Technology - Beijing","correspondingAuthor":false,"prefix":"","firstName":"Zongnan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-09-27 06:15:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5162658/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5162658/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11600-025-01614-5","type":"published","date":"2025-06-03T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84243182,"identity":"03b66f80-b826-4a45-b2bc-c08f106572cc","added_by":"auto","created_at":"2025-06-09 16:12:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1500315,"visible":true,"origin":"","legend":"","description":"","filename":"Mansucript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5162658/v1_covered_5cc560c4-82da-4e12-9b3e-07642f7dde9f.pdf"}],"financialInterests":"","formattedTitle":"Diffraction separation method using shapeDTW and median-mean filter","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"diffraction separation, shapeDTW, flattened reflection gathers, median-mean filter","lastPublishedDoi":"10.21203/rs.3.rs-5162658/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5162658/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe subsurface small-scale geological structures are manifested as diffractions in seismic data. 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