OBF Decomposition and Detection of Aerial Magnetic Anomaly Based on Support Vector Machine

OBF Decomposition and Detection of Aerial Magnetic Anomaly Based on Support Vector Machine | 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 Method Article OBF Decomposition and Detection of Aerial Magnetic Anomaly Based on Support Vector Machine Zhentao Yu, Pu Cheng, Cheng Chi, Chun Zhou, Tongyu Li, Liming Yuan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6898591/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 When the orthogonal basis function (OBF) is used to decompose and detect the aeromagnetic anomaly, the unreasonable proportion of the decomposition coefficient leads to a high false alarm rate in the case of low signal-to-noise ratio. Therefore, the OBF decomposition detection method based on support vector machine (SVM) is proposed. This method uses the SVM pattern recognition method to learn OBF decomposition coefficient characteristics of the target samples and the background noise samples, and optimizes the proportion of each OBF decomposition coefficient in the detection statistics through machine learning, thus realizing target magnetic anomaly detection through the SVM classification. The method is tested and verified by using test samples of measured background noise and simulated target signals. The results show that the proposed method can effectively improve the detection efficiency of OBF decomposition detection method. Aerial Magnetic Anomaly Support Vector Machine Target Detection The Orthogonal Basis Function Magnetic Background Noise 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-6898591","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":477857099,"identity":"55ec3cd4-8c9c-4caa-a9af-419704627583","order_by":0,"name":"Zhentao Yu","email":"","orcid":"","institution":"Naval submarine academy","correspondingAuthor":false,"prefix":"","firstName":"Zhentao","middleName":"","lastName":"Yu","suffix":""},{"id":477857100,"identity":"31ed7758-ad92-4063-b25f-1b3d47669d58","order_by":1,"name":"Pu 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