Automated offset detection approaches: Case study in IGS Repro2 and 3

Automated offset detection approaches: Case study in IGS Repro2 and 3 | 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 Automated offset detection approaches: Case study in IGS Repro2 and 3 Jin Zhang, LiZhen Lian, ChengLi Huang, CanCan Xu, SiMeng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3813244/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 May, 2024 Read the published version in GPS Solutions → Version 1 posted 4 You are reading this latest preprint version Abstract Due to their high false positive detection rates, traditional automated offset detections on GNSS station coordinate time series still cannot fully replace manual detections in practical applications and need to be improved. This work proposes preliminary and enhanced automated offset detection approaches for IGS Repro2 and Repro3 datasets. Their manually detected offset recordings in ITRF2014 and ITRF2020 are used as evaluation criteria. In the preliminary approaches, stochastic models based on covariance matrix, white noise model, and white noise plus flicker noise model of both univariate and multivariate are studied. Although a somewhat “good” performance is achieved, the preliminary automated detections still lead to many false positive detections. Thus, based on the preliminary approaches, and ancillary data, an enhanced detection approach is proposed. Enhanced detections significantly reduce 56%~80% false positive detections compared to preliminary approaches. As a result, for Repro3, the optimal overall performance is attained with true positive, false positive, and false negative rates (TP, FP, FN) of (0.57, 0.25, 0.18), along with a detection rate of 75%; for Repro2, the rates are (0.58, 0.20, 0.22), accompanied by a 73% detection rate. According to its “good” performance mentioned above, the current enhanced approach may serve as a supplementary or reference to manual detection, although still not being perfect. Furthermore, twenty manually detected unknown offsets in ITRF2020 are found to correspond to some known events (thirteen earthquakes and seven equipment changes); thirty-four automated detections that correspond to known events but are not collected in ITRF2020 are manually checked as offsets (fourteen earthquakes and twenty equipment changes). automated offset detection noise analysis multivariate analysis ITRF Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 May, 2024 Read the published version in GPS Solutions → Version 1 posted Editorial decision: Revision requested 29 Dec, 2023 Editor assigned by journal 29 Dec, 2023 Submission checks completed at journal 28 Dec, 2023 First submitted to journal 27 Dec, 2023 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-3813244","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264396518,"identity":"2268f824-27e2-40ef-a4b7-e4b9fe39eb91","order_by":0,"name":"Jin Zhang","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Zhang","suffix":""},{"id":264396519,"identity":"ec2e7319-b05d-4c84-a31f-374da754ef8b","order_by":1,"name":"LiZhen Lian","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"LiZhen","middleName":"","lastName":"Lian","suffix":""},{"id":264396520,"identity":"6d72e4ab-a391-4afe-99f6-64d6eefc0b2c","order_by":2,"name":"ChengLi Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACdh4wJccgQbQWZogWY9K1JDYQrYW/mfeYxM8dtenzZzc/fMBQY8fAP7sBvxaJw3xpkr1njuduuHPM2IDhWDKDxJ0D+LUYMPOYSfC2HcvdIJFgJsHAdoDBQCKBsBbJv23H0uVnpH+TYPhHpBZp3raaBIYbOWYSjG1EaJE4zGNsLdt2wHDDjZxig8S+ZB6JGwS08Lf3GN5821YnD3TYxgcfvtnJ8c8goAUKDkMooGIeotQDQR2xCkfBKBgFo2AkAgA9zzs99SznBgAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"ChengLi","middleName":"","lastName":"Huang","suffix":""},{"id":264396521,"identity":"242fe207-4173-40d9-b7ab-b9326b9294f9","order_by":3,"name":"CanCan Xu","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"CanCan","middleName":"","lastName":"Xu","suffix":""},{"id":264396522,"identity":"a89ba15a-e6b6-4d5d-9ef9-7e7aed926355","order_by":4,"name":"SiMeng Zhang","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"SiMeng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-12-27 17:44:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3813244/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3813244/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10291-024-01662-2","type":"published","date":"2024-05-13T20:22:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61526296,"identity":"4018d83a-a0e7-4a4a-a2b4-2a7602c38aa5","added_by":"auto","created_at":"2024-07-31 20:23:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1167534,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3813244/v1_covered_d6de6ba5-86c9-4f5d-8123-4c9ee55c78e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Automated offset detection approaches: Case study in IGS Repro2 and 3","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"gps-solutions","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gpss","sideBox":"Learn more about [GPS Solutions](http://link.springer.com/journal/10291)","snPcode":"10291","submissionUrl":"https://submission.nature.com/new-submission/10291/3","title":"GPS Solutions","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"automated offset detection, noise analysis, multivariate analysis, ITRF","lastPublishedDoi":"10.21203/rs.3.rs-3813244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3813244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to their high false positive detection rates, traditional automated offset detections on GNSS station coordinate time series still cannot fully replace manual detections in practical applications and need to be improved. This work proposes preliminary and enhanced automated offset detection approaches for IGS Repro2 and Repro3 datasets. Their manually detected offset recordings in ITRF2014 and ITRF2020 are used as evaluation criteria. In the preliminary approaches, stochastic models based on covariance matrix, white noise model, and white noise plus flicker noise model of both univariate and multivariate are studied. Although a somewhat \u0026ldquo;good\u0026rdquo; performance is achieved, the preliminary automated detections still lead to many false positive detections. Thus, based on the preliminary approaches, and ancillary data, an enhanced detection approach is proposed. Enhanced detections significantly reduce 56%~80% false positive detections compared to preliminary approaches. 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