Enhanced GPR Imaging Using High-Resolution TR-MUSIC for Underground Object Localization

Enhanced GPR Imaging Using High-Resolution TR-MUSIC for Underground Object Localization | 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 Enhanced GPR Imaging Using High-Resolution TR-MUSIC for Underground Object Localization Hamidreza Karami, Carlos Romero, Marcos Rubinstein, Farhad Rachidi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8347199/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract In this paper, we present a novel, high-resolution method, referred to as HRTR, for localizing underground objects. HRTR is based on a combination of the Time Reversal (TR) and Multiple Signal Classification (MUSIC) algorithms, and can be readily integrated with conventional ground-penetrating radar (GPR) systems without requiring any additional hardware. The proposed method offers significant advantages, particularly in achieving higher resolution, which enhances the ability to distinguish ground surface reflections and detect shallowly buried objects—challenges often encountered with conventional methods. The theoretical foundation of the proposed method is validated through numerical simulations using gprMax, as well as through experimental measurements from laboratory and field tests. The performance of HRTR is compared with conventional GPR methods, focusing on resolution improvements. Both simulations and experimental results demonstrate that HRTR produces clearer, sharper images with enhanced resolution. Unlike classical TR-MUSIC, the proposed HRTR method can be applied directly to conventional GPR measurements without the need for additional hardware or intensive computation.Moreover, it operates with just one antenna in monostatic mode or two in bistatic mode, avoiding the multiple-antenna requirement of TR-MUSIC. Furthermore, the proposed method enables the detection of deeply buried objects by using low-frequency signals for greater penetration while preserving spatial resolution. A graphical user interface was also developed and made available on GitHub for applying the proposed method to GPR A- and B-scans. Physical sciences/Engineering Physical sciences/Optics and photonics Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Supplementary Files SciRepSIv05.docx Cite Share Download PDF Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Mar, 2026 Reviews received at journal 07 Mar, 2026 Reviews received at journal 20 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 20 Dec, 2025 Editor invited by journal 19 Dec, 2025 Submission checks completed at journal 18 Dec, 2025 First submitted to journal 18 Dec, 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-8347199","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":590756849,"identity":"25b258b0-d241-4007-83fe-1de582dac550","order_by":0,"name":"Hamidreza Karami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACAyA+wANmsoFJHn4g8eEBA0MC8VokGxgYZyQQ0MKArIXB4AABLebspxMPvN1jY8/fwJb4uaJmm4zx8d6DDQkMdnm4tFj25G44OOdZGrPEAbbDkmeO3eYxO3MuEagluRinww7kbjjMc+AwG8MB9gbJBjaglhs55g8SGA4kNuDScv4tSMt/HvkD7M0/G/7d5jGe/8awAa+WG2BbDkgYHGA7JtnYdpvHQIKHkJa3QL8cSDYwPMyWZtnYd5tH4kwe0C8GyXgclrv5w5sDdvZyx9uMbzZ8u23P3372YMOHCjucWhCAGc4CRZMBQfUogIc05aNgFIyCUTDsAQAeomN4onOf3AAAAABJRU5ErkJggg==","orcid":"","institution":"École Polytechnique Fédérale de Lausanne","correspondingAuthor":true,"prefix":"","firstName":"Hamidreza","middleName":"","lastName":"Karami","suffix":""},{"id":590756850,"identity":"b5c69532-29e0-4f7a-aeb0-bf5b668670db","order_by":1,"name":"Carlos Romero","email":"","orcid":"","institution":"Federal Department of Defence, Civil Protection and Sports","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Romero","suffix":""},{"id":590756851,"identity":"0a741871-a319-4ef7-881b-11ca60e1cf39","order_by":2,"name":"Marcos Rubinstein","email":"","orcid":"","institution":"HES-SO Vaud","correspondingAuthor":false,"prefix":"","firstName":"Marcos","middleName":"","lastName":"Rubinstein","suffix":""},{"id":590756852,"identity":"c02ca678-27a3-4e62-bb98-3dec88009227","order_by":3,"name":"Farhad Rachidi","email":"","orcid":"","institution":"École Polytechnique Fédérale de Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Farhad","middleName":"","lastName":"Rachidi","suffix":""}],"badges":[],"createdAt":"2025-12-12 15:38:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8347199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8347199/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-49191-x","type":"published","date":"2026-04-28T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":108437635,"identity":"24b0fd86-423b-49b5-9368-7f23c6e5a56b","added_by":"auto","created_at":"2026-05-04 16:01:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7039600,"visible":true,"origin":"","legend":"","description":"","filename":"xtywkzrpkttxpyxwgskmnnmjtkvzjgqw.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8347199/v1_covered_e5b88ae8-89e1-4c2c-9a98-0cb7d715794c.pdf"},{"id":102728832,"identity":"00396d35-7679-4b12-9c99-5db1e94055e5","added_by":"auto","created_at":"2026-02-16 03:44:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6238491,"visible":true,"origin":"","legend":"","description":"","filename":"SciRepSIv05.docx","url":"https://assets-eu.researchsquare.com/files/rs-8347199/v1/e10e1dfb8554766f57ff9e65.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced GPR Imaging Using High-Resolution TR-MUSIC for Underground Object Localization","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":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8347199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8347199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this paper, we present a novel, high-resolution method, referred to as HRTR, for localizing underground objects. 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