YOLOv11-LWMB: A Hybrid YOLOv11 Framework for the Detection of ' Bull's-Eye ' Effect in Seismic Data

YOLOv11-LWMB: A Hybrid YOLOv11 Framework for the Detection of ' Bull's-Eye ' Effect in Seismic Data | 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 YOLOv11-LWMB: A Hybrid YOLOv11 Framework for the Detection of ' Bull's-Eye ' Effect in Seismic Data Qijing Chen, Li Pan, Meng Jiabing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7817064/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 6 You are reading this latest preprint version Abstract Accurate identification of ring-shaped amplitude artifacts, commonly referred to as the “Bull’s-Eye” effect, remains a key challenge in seismic inversion due to low-frequency deficiencies and complex subsurface heterogeneity. To address this issue, a hybrid lightweight framework named YOLOv11-LWMB is proposed for the automatic detection of Bull’s-Eye anomalies in post-stack seismic images. The model introduces a multi-component design in which MobileNetV4 enhances feature extraction efficiency, C3k2-WTConv expands the receptive field and strengthens low-frequency texture perception, LSKAttention adaptively models spatial context through large-kernel decomposition, and BiFPN realizes bidirectional multi-scale fusion for consistent anomaly localization. Extensive experiments on real seismic datasets demonstrate that YOLOv11-LWMB achieves substantial improvements over the original YOLOv11 baseline, with increases of 5.9 % in precision, 10.0 % in recall, and 12.8 % in [email protected] , while maintaining fast inference and low computational demand. These results confirm the model’s robustness in detecting weak and blurred seismic anomalies and highlight its potential for intelligent quality control and automatic interpretation in seismic inversion workflows. Seismic inversion anomaly detection Bull’s-eye effect YOLOv11 Multi-scale feature fusion Full Text Cite Share Download PDF Status: Under Revision Version 1 posted Reviewers agreed at journal 21 Nov, 2025 Reviewers invited by journal 20 Nov, 2025 Editor invited by journal 14 Nov, 2025 Editor assigned by journal 13 Nov, 2025 First submitted to journal 10 Nov, 2025 Editorial decision: Major revisions 20 Oct, 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. 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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-7817064","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":548104285,"identity":"135a291f-b120-4d00-b056-1591128456a2","order_by":0,"name":"Qijing 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