Objective and automated determination of sharp resistivity boundaries in one-dimensional magnetotelluric inversion

Objective and automated determination of sharp resistivity boundaries in one-dimensional magnetotelluric inversion | 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 Objective and automated determination of sharp resistivity boundaries in one-dimensional magnetotelluric inversion Keiichi Ishizu, Yukitoshi Fukahata This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9391412/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract This study presents a fully data-driven Sharp Boundary Inversion (SBI) algorithm for one-dimensional magnetotelluric data to objectively delineate abrupt subsurface resistivity transitions. Conventional smoothness-regularized inversions suffer from unnatural blurring of discontinuous features, such as faults and lithological contacts. To overcome this limitation, the proposed SBI framework utilizes Akaike Bayesian Information Criterion (ABIC) as a rigorous structural model-selection metric. We introduce a novel two-stage optimization architecture in which an outer loop leveraging the Optuna hyperparameter optimization framework explores the optimal locations and relaxation parameters of sharp boundaries, and an inner Gauss–Newton loop iteratively updates the resistivity model, concurrently determining the global smoothing hyperparameter via ABIC minimization. This integrated scheme autonomously determines the optimal boundary depths and localized smoothing penalties without relying on subjective manual tuning or detailed a priori information. Numerical experiments demonstrate the stability of the algorithm, localizing boundaries with a maximum deviation of a single 50-m layer under severe 10% noise conditions. Crucially, SBI faithfully reconstructs complex subsurface structures featuring both smooth gradients and abrupt jumps, without generating the staircase artifacts that are common to global sharpening techniques. Released as an open-source tool, this algorithm provides a highly robust and objective foundation for subsurface structure interpretation in electromagnetic geophysics. Magnetotellurics Sharp boundary inversion Akaike Bayesian Information Criterion Optuna One-dimensional inversion Data-driven inversion Full Text Supplementary Files Gabst.png Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Minor Revision 25 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 14 Apr, 2026 First submitted to journal 11 Apr, 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. 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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-9391412","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626574495,"identity":"2119ea93-533c-4dd4-903c-342295340c88","order_by":0,"name":"Keiichi Ishizu","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-3663-182X","institution":"Kyushu University: Kyushu Daigaku","correspondingAuthor":true,"prefix":"","firstName":"Keiichi","middleName":"","lastName":"Ishizu","suffix":""},{"id":626574496,"identity":"6922921f-d154-4d7e-96d1-74e102d8f079","order_by":1,"name":"Yukitoshi Fukahata","email":"","orcid":"","institution":"Kyoto University - Uji Campus: Kyoto Daigaku - Uji Campus","correspondingAuthor":false,"prefix":"","firstName":"Yukitoshi","middleName":"","lastName":"Fukahata","suffix":""}],"badges":[],"createdAt":"2026-04-12 03:49:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9391412/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9391412/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108183183,"identity":"2e9dec29-ba93-4bed-b03e-fc16a3215bf2","added_by":"auto","created_at":"2026-04-30 08:59:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":848500,"visible":true,"origin":"","legend":"","description":"","filename":"renamed3e95f.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9391412/v1_covered_27657b9d-0dfc-4a2f-8f49-9b1e365ff284.pdf"},{"id":108148868,"identity":"efe147ae-320e-4424-b04f-05a19e084e20","added_by":"auto","created_at":"2026-04-29 22:56:33","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":530072,"visible":true,"origin":"","legend":"","description":"","filename":"Gabst.png","url":"https://assets-eu.researchsquare.com/files/rs-9391412/v1/32ef746e1a2713da6ea84797.png"}],"financialInterests":"","formattedTitle":"Objective and automated determination of sharp resistivity boundaries in one-dimensional magnetotelluric inversion","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":"earth-planets-and-space","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epsp","sideBox":"Learn more about [Earth, Planets and Space](http://earth-planets-space.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/epsp/default.aspx","title":"Earth, Planets and Space","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Magnetotellurics, Sharp boundary inversion, Akaike Bayesian Information Criterion, Optuna, One-dimensional inversion, Data-driven inversion","lastPublishedDoi":"10.21203/rs.3.rs-9391412/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9391412/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a fully data-driven Sharp Boundary Inversion (SBI) algorithm for one-dimensional magnetotelluric data to objectively delineate abrupt subsurface resistivity transitions. Conventional smoothness-regularized inversions suffer from unnatural blurring of discontinuous features, such as faults and lithological contacts. To overcome this limitation, the proposed SBI framework utilizes Akaike Bayesian Information Criterion (ABIC) as a rigorous structural model-selection metric. We introduce a novel two-stage optimization architecture in which an outer loop leveraging the Optuna hyperparameter optimization framework explores the optimal locations and relaxation parameters of sharp boundaries, and an inner Gauss\u0026ndash;Newton loop iteratively updates the resistivity model, concurrently determining the global smoothing hyperparameter via ABIC minimization. This integrated scheme autonomously determines the optimal boundary depths and localized smoothing penalties without relying on subjective manual tuning or detailed \u003cem\u003ea priori\u003c/em\u003e information. 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