Combining Gravity Data and Gaussian Elliptical Modeling for Improved Seamount Morphology Estimation

Combining Gravity Data and Gaussian Elliptical Modeling for Improved Seamount Morphology Estimation | 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 Combining Gravity Data and Gaussian Elliptical Modeling for Improved Seamount Morphology Estimation Huan Xu, He Tang, Bing Zheng, Chao Dong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9105899/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 Estimating seamount morphology with high accuracy is essential for interpreting marine geodynamics and improving gravity-based seafloor mapping. This study develops an inversion framework that combines Gaussian and Gaussian elliptical cone models with a grid-search algorithm, utilizing sea surface gravity anomaly (VG) and vertical gravity gradient anomaly (VGG) data. Numerical experiments and real-world case studies show that modeling seamounts as Gaussian shapes tends to underestimate summit height and overestimate base size, due to the Gaussian model’s flatter slope near the summit. The Gaussian elliptical cone model, by introducing two additional parameters to capture elliptical base shapes, reduces these errors and improves estimation accuracy by 10.3%–37.5% compared to the Gaussian cone. Moreover, VGG anomalies, which are more sensitive to high-frequency morphological features and less affected by crustal elastic thickness, improve inversion accuracy by 18.6%–46.5% over VG anomalies. These results demonstrate the effectiveness of combining VGG data and Gaussian elliptical modeling in mitigating summit underestimation and better representing seamount morphology. Seamount Morphology Gaussian Cone Gaussian elliptical cone Gravity anomaly Gravity gradient anomaly Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 06 May, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 14 Mar, 2026 First submitted to journal 12 Mar, 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. 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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-9105899","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638563194,"identity":"691daf70-f81a-48a4-80b5-40192201106a","order_by":0,"name":"Huan Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYFACHhBhQ7qWNNK1HCZBg8GN3GMSP3ecT+xvP/6A4UcNg7w5IS2SM/LSJHvP3E6ccSbHgLHnGIPhzgYCWvglcswkeNtuJ25gyGFg4G1gSDA4QEALG1CL5N+2c4kb+J8/YPxLjBaQLdK8bQcSN0gkGDATZYtkzxtja9m2ZOMZN94YHJY5JmG4gZAWg+M5hjffttnJ9venP3z4psZGnqAtKACoWIIU9aNgFIyCUTAKcAEAyy88vMPIyfsAAAAASUVORK5CYII=","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":true,"prefix":"","firstName":"Huan","middleName":"","lastName":"Xu","suffix":""},{"id":638563197,"identity":"8fc7062e-d861-4022-a1f3-a685c54496ca","order_by":1,"name":"He Tang","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Tang","suffix":""},{"id":638563199,"identity":"7f76b37b-c694-4875-939c-14b4f99fcb95","order_by":2,"name":"Bing Zheng","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Zheng","suffix":""},{"id":638563200,"identity":"05398cfd-ec86-4542-b067-8e94f80dcf32","order_by":3,"name":"Chao Dong","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2026-03-12 14:24:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9105899/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9105899/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109296346,"identity":"c62244f8-8e38-409e-8367-59b3cf1e9818","added_by":"auto","created_at":"2026-05-15 08:46:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1742474,"visible":true,"origin":"","legend":"","description":"","filename":"NewManuscriptCombiningGravityDataandGaussianEllipticalModelingforImprovedSeamountMorphologyEstimation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9105899/v1_covered_54fbba24-a7ab-4970-a314-437bf5c9e3f6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combining Gravity Data and Gaussian Elliptical Modeling for Improved Seamount Morphology Estimation","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":"pure-and-applied-geophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"paag","sideBox":"Learn more about [Pure and Applied Geophysics](https://www.springer.com/journal/24)","snPcode":"24","submissionUrl":"https://submission.nature.com/new-submission/24/3","title":"Pure and Applied Geophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Seamount Morphology, Gaussian Cone, Gaussian elliptical cone, Gravity anomaly, Gravity gradient anomaly","lastPublishedDoi":"10.21203/rs.3.rs-9105899/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9105899/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEstimating seamount morphology with high accuracy is essential for interpreting marine geodynamics and improving gravity-based seafloor mapping. This study develops an inversion framework that combines Gaussian and Gaussian elliptical cone models with a grid-search algorithm, utilizing sea surface gravity anomaly (VG) and vertical gravity gradient anomaly (VGG) data. Numerical experiments and real-world case studies show that modeling seamounts as Gaussian shapes tends to underestimate summit height and overestimate base size, due to the Gaussian model\u0026rsquo;s flatter slope near the summit. The Gaussian elliptical cone model, by introducing two additional parameters to capture elliptical base shapes, reduces these errors and improves estimation accuracy by 10.3%\u0026ndash;37.5% compared to the Gaussian cone. Moreover, VGG anomalies, which are more sensitive to high-frequency morphological features and less affected by crustal elastic thickness, improve inversion accuracy by 18.6%\u0026ndash;46.5% over VG anomalies. 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