CEEMD-UNet: Feature-Preserving Deep-Denoising Method for High-rate GNSS coseismic displacement

CEEMD-UNet: Feature-Preserving Deep-Denoising Method for High-rate GNSS coseismic displacement | 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 CEEMD-UNet: Feature-Preserving Deep-Denoising Method for High-rate GNSS coseismic displacement Yanyan Li, Qiurong Jiang, Rui Tu, Xiangxiang Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8781246/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract High‑rate Global Navigation Satellite System (GNSS) observations provide direct measurements of surface coseismic displacements and are a key tool for seismic monitoring. However, the signal‑to‑noise ratio (SNR) of high‑rate GNSS coseismic displacement data is often low in the case of moderate‑magnitude or distant earthquakes, and traditional denoising methods struggle to balance noise suppression with signal preservation. To overcome this problem, this study proposes a new feature-preserving deep-denoising method based on complementary ensemble empirical mode decomposition (CEEMD) and UNet network (CEEMD‑UNet). The proposed method first adaptively decomposes the non‑stationary coseismic displacement via CEEMD, effectively overcoming the limitations of conventional denoising techniques. The resulting intrinsic mode functions (IMFs) are then accurately denoised using a UNet network, achieving an effective balance between noise removal and preservation of coseismic signal features. Tests on synthetic data show that after denoising, the average cross‑correlation coefficients in the E, N, and U directions all exceed 0.85, and the average SNR is improved by factors of 14.72, 12.84, and 38.77, respectively. Validation using real GNSS data from the 2018 Mw 7.0 Anchorage, Alaska earthquake indicates that the average root mean square error across stations is reduced by 41.39%, 48.79%, and 67.90% in the three components, while the SNR is increased by factors of 5.05, 2.89, and 0.96. Compared with CEEMD‑WD and UNet‑only denoising methods, the proposed approach demonstrates significant advantages in waveform consistency, amplitude preservation, and cross‑scenario adaptability, offering a reliable technical solution for processing low‑SNR high‑rate GNSS seismic data. High-rate GNSS denosing CEEMD Deep learning Convolutional Neural Network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers invited by journal 31 Mar, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 06 Feb, 2026 First submitted to journal 03 Feb, 2026 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-8781246","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615390186,"identity":"45bc0a4b-6cb6-44e7-9c11-44f1949f8c2e","order_by":0,"name":"Yanyan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFAC5gYGhgoGxgYwh40oLSDFZ0AkMylaGNtI0WJwI7Hxwcd5dbIbzp8/wPCh7DAD/+wGglqaDWduO2y84UYyA+OMc4cZJO4cIKilTZp324HEDTeYGZh52w4zGEgkENTS/vvvnLrEDecPMzD/JVJLGzNjA3PihgPJDMyMxGiRPPOwWbLn2GHjmTeSDQ72nEvnkbhBQAvf8eSDH37U1Mn2nT/48MGPMms5/hkEtChcQFJwAIh58KsHAvn+AwTVjIJRMApGwUgHAADkTNbtEVLUAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Li","suffix":""},{"id":615390188,"identity":"1c653d0d-93e6-48ed-89d8-eaa423079f87","order_by":1,"name":"Qiurong Jiang","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qiurong","middleName":"","lastName":"Jiang","suffix":""},{"id":615390190,"identity":"22a5197e-1b45-4dbf-b084-d32104798364","order_by":2,"name":"Rui Tu","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Tu","suffix":""},{"id":615390191,"identity":"73aa823b-bf9e-4b62-9373-831628122a8b","order_by":3,"name":"Xiangxiang Liu","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiangxiang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-02-04 03:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8781246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8781246/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106095085,"identity":"27ba6736-19bd-44b6-bf37-53feaf879ea1","added_by":"auto","created_at":"2026-04-03 11:44:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1722598,"visible":true,"origin":"","legend":"","description":"","filename":"CEEMDUNetFeaturePreservingDeepDenoisingMethodforHighrateGNSScoseismicdisplacement20260204.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8781246/v1_covered_68832093-459c-45b9-9e3e-827180be4430.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CEEMD-UNet: Feature-Preserving Deep-Denoising Method for High-rate GNSS coseismic displacement","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"High-rate GNSS, denosing, CEEMD, Deep learning, Convolutional Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-8781246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8781246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh‑rate Global Navigation Satellite System (GNSS) observations provide direct measurements of surface coseismic displacements and are a key tool for seismic monitoring. However, the signal‑to‑noise ratio (SNR) of high‑rate GNSS coseismic displacement data is often low in the case of moderate‑magnitude or distant earthquakes, and traditional denoising methods struggle to balance noise suppression with signal preservation. To overcome this problem, this study proposes a new feature-preserving deep-denoising method based on complementary ensemble empirical mode decomposition (CEEMD) and UNet network (CEEMD‑UNet). The proposed method first adaptively decomposes the non‑stationary coseismic displacement via CEEMD, effectively overcoming the limitations of conventional denoising techniques. The resulting intrinsic mode functions (IMFs) are then accurately denoised using a UNet network, achieving an effective balance between noise removal and preservation of coseismic signal features. Tests on synthetic data show that after denoising, the average cross‑correlation coefficients in the E, N, and U directions all exceed 0.85, and the average SNR is improved by factors of 14.72, 12.84, and 38.77, respectively. Validation using real GNSS data from the 2018 Mw 7.0 Anchorage, Alaska earthquake indicates that the average root mean square error across stations is reduced by 41.39%, 48.79%, and 67.90% in the three components, while the SNR is increased by factors of 5.05, 2.89, and 0.96. Compared with CEEMD‑WD and UNet‑only denoising methods, the proposed approach demonstrates significant advantages in waveform consistency, amplitude preservation, and cross‑scenario adaptability, offering a reliable technical solution for processing low‑SNR high‑rate GNSS seismic data.\u003c/p\u003e","manuscriptTitle":"CEEMD-UNet: Feature-Preserving Deep-Denoising Method for High-rate GNSS coseismic displacement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 07:28:13","doi":"10.21203/rs.3.rs-8781246/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-14T18:58:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152110805401312110578464477133249082565","date":"2026-03-31T15:16:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T08:27:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T01:59:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-06T06:45:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"GPS Solutions","date":"2026-02-04T03:10:37+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"5c945291-61d0-49e1-ba32-8c5ee1ef2147","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T07:28:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 07:28:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8781246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8781246","identity":"rs-8781246","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}