3-D Seismic Fault Identification Using Dual-Decoder Network With Multi-Feature Fusion

3-D Seismic Fault Identification Using Dual-Decoder Network With Multi-Feature Fusion | 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 3-D Seismic Fault Identification Using Dual-Decoder Network With Multi-Feature Fusion Lili Zeng, Limin Dai, Zhiyuan Wei, Jianpeng Zhang, Zhaonan Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7622248/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Mar, 2026 Read the published version in Earth Science Informatics → Version 1 posted 9 You are reading this latest preprint version Abstract Accurate identification of seismic faults is an important step in the development process of underground oil and gas reservoirs. Although semantic segmentation technology has been widely applied in the field of fault identification, this method still has problems such as blurred fault features, discontinuous identification of fault lines, and misidentification and missed identification. In response to these problems, this paper proposes a multi-scale feature fusion intelligent fault identification with a dual-decoder architecture. Based on the U shaped encoder-decoder architecture, the method constructs a dual-decoder network. The feature fusion of the dual-decoder results ensures comprehensive fault information reconstruction and continuous fault identification. Meanwhile, a three-channel fusion feature module is designed to extract the pooling features, convolution features and residual features of the input seismic data, highlighting the target fault line and enhancing the model's identification ability on complex faults. Develop a four-scale kernel path module to replace the traditional skip connection operation, utilizing multi-kernel path to adapt to hierarchical fault features, and enhance the model's extraction ability of edge hidden faults and long-distance faults. The accuracy of this method in fault identification tasks on synthetic datasets reached 95.03%, which is 1.8% higher than the U-Net network with a single encoder-decoder structure.. The experimental results on the F3 dataset in the Netherlands show that this method provides clearer and more accurate characterization of fault lines in real seismic data. It effectively reduces false identifications and missed detection of faults, while improving the model's performance in fault localization and segmentation capabilities across multi-scale fault structures. Fault identification Dual decoder Deep learning Feature fusion Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Mar, 2026 Read the published version in Earth Science Informatics → Version 1 posted Editorial decision: Revision requested 25 Jan, 2026 Reviews received at journal 19 Jan, 2026 Reviews received at journal 14 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviewers agreed at journal 27 Dec, 2025 Reviewers invited by journal 28 Sep, 2025 Editor assigned by journal 28 Sep, 2025 Submission checks completed at journal 24 Sep, 2025 First submitted to journal 15 Sep, 2025 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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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-7622248","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526545204,"identity":"698fb9b4-93a8-4fce-835e-64bd08669c4a","order_by":0,"name":"Lili 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Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Fault identification, Dual decoder, Deep learning, Feature fusion","lastPublishedDoi":"10.21203/rs.3.rs-7622248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7622248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate identification of seismic faults is an important step in the development process of underground oil and gas reservoirs. Although semantic segmentation technology has been widely applied in the field of fault identification, this method still has problems such as blurred fault features, discontinuous identification of fault lines, and misidentification and missed identification. In response to these problems, this paper proposes a multi-scale feature fusion intelligent fault identification with a dual-decoder architecture. Based on the U shaped encoder-decoder architecture, the method constructs a dual-decoder network. The feature fusion of the dual-decoder results ensures comprehensive fault information reconstruction and continuous fault identification. Meanwhile, a three-channel fusion feature module is designed to extract the pooling features, convolution features and residual features of the input seismic data, highlighting the target fault line and enhancing the model's identification ability on complex faults. Develop a four-scale kernel path module to replace the traditional skip connection operation, utilizing multi-kernel path to adapt to hierarchical fault features, and enhance the model's extraction ability of edge hidden faults and long-distance faults. The accuracy of this method in fault identification tasks on synthetic datasets reached 95.03%, which is 1.8% higher than the U-Net network with a single encoder-decoder structure.. The experimental results on the F3 dataset in the Netherlands show that this method provides clearer and more accurate characterization of fault lines in real seismic data. It effectively reduces false identifications and missed detection of faults, while improving the model's performance in fault localization and segmentation capabilities across multi-scale fault structures.\u003c/p\u003e","manuscriptTitle":"3-D Seismic Fault Identification Using Dual-Decoder Network With Multi-Feature Fusion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-09 17:57:14","doi":"10.21203/rs.3.rs-7622248/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-25T17:38:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-19T18:58:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T14:01:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216802246649475252137210654329536644578","date":"2025-12-30T03:57:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163240966799106594490498060643922627958","date":"2025-12-28T00:08:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-28T13:08:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-28T13:06:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-24T08:53:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Earth Science Informatics","date":"2025-09-15T15:20:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"earth-science-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esin","sideBox":"Learn more about [Earth Science Informatics](http://link.springer.com/journal/12145)","snPcode":"12145","submissionUrl":"https://submission.nature.com/new-submission/12145/3","title":"Earth Science Informatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"560bed17-d669-4946-8b39-7d14a0b57008","owner":[],"postedDate":"October 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:33:55+00:00","versionOfRecord":{"articleIdentity":"rs-7622248","link":"https://doi.org/10.1007/s12145-026-02103-z","journal":{"identity":"earth-science-informatics","isVorOnly":false,"title":"Earth Science Informatics"},"publishedOn":"2026-03-24 16:10:24","publishedOnDateReadable":"March 24th, 2026"},"versionCreatedAt":"2025-10-09 17:57:14","video":"","vorDoi":"10.1007/s12145-026-02103-z","vorDoiUrl":"https://doi.org/10.1007/s12145-026-02103-z","workflowStages":[]},"version":"v1","identity":"rs-7622248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7622248","identity":"rs-7622248","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}