A Precise Time-Depth Conversion Method for Coal Seam Based on Machine Learning and Seismic Velocity Inversion

A Precise Time-Depth Conversion Method for Coal Seam Based on Machine Learning and Seismic Velocity 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 A Precise Time-Depth Conversion Method for Coal Seam Based on Machine Learning and Seismic Velocity Inversion Hang Yu, Haibo Wang, Leibing Wu, Tongjun Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4433498/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 May, 2025 Read the published version in Acta Geophysica → Version 1 posted 6 You are reading this latest preprint version Abstract Time-depth conversion is a crucial step in 3D seismic interpretation of coalfields. Fast and accurate time-depth conversion is essential for ensuring safe and efficient coal production. However, conventional methods often struggle to balance accuracy with convenience, which makes it difficult to achieve good application results in the coalfield. To address this problem, we proposed a new coal seam time-depth conversion method based on machine learning and seismic velocity inversion. Firstly, a high-precision time-domain layer of the coal seam floor was obtained. Subsequently, the average velocity of the coal seam floor was calculated from boreholes. Following this, post-stack seismic inversion was performed to obtain velocity volumes, and the velocity volumes were subjected to median filtering. Next, machine learning models were trained using the average velocity of the coal seam floor extracted from inverted velocity volume, the average velocity of the coal seam floor calculated and interpolated by control boreholes, and two-way travel times (TWTs) of the coal seam floor as inputs, with actual coal seam floor elevations as the outputs. Finally, different machine learning methods and conventional methods were compared and analyzed for time-depth conversion in coalfield. The results indicate that the Bayesian-SVR model achieved the highest accuracy in time-depth conversion, with a maximum absolute error of only 1.11 meters and a mean absolute error of 0.53 meters at verification boreholes. In summary, this study introduces a machine learning-based coal seam time-depth conversion method that does not require complex velocity models, enhancing efficiency while maintaining high accuracy, which holds significant importance for advancing intelligent coal mining and achieving transparent working faces. Time-depth conversion Coal seam Average velocity Seismic velocity inversion Machine learning Full Text Cite Share Download PDF Status: Published Journal Publication published 15 May, 2025 Read the published version in Acta Geophysica → Version 1 posted Editorial decision: Major revisions 01 Nov, 2024 Reviewers agreed at journal 03 Jul, 2024 Reviewers invited by journal 03 Jul, 2024 Editor invited by journal 19 Jun, 2024 Editor assigned by journal 26 May, 2024 First submitted to journal 19 May, 2024 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-4433498","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322239515,"identity":"8212fe99-72cd-46ae-b7b7-710e9625d7c3","order_by":0,"name":"Hang Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYPCCA0DMfODAhx/EKWdsgGhhSzw4s4c0LTzGhznYiFDPPyM7/TFv2x15c/41Hw4z8DDI84sdwK9F4kbuxmbetmeGO2e83XC4wILBcObsBPxaDCTAWg4zbrhxdsPhGTwMCQa3idRiv+HGmQeHedhI0JK44XwPA3FaJM683ThzzrnDyRtusBkAA1mCsF/423M3fHhTdth2w/nDjz98+GEjzy9NQAsIMPGA7QOrlCCsHAQYwcmE/wBxqkfBKBgFo2DkAQBp+VCQpMvtZwAAAABJRU5ErkJggg==","orcid":"","institution":"China University of Mining and Technology","correspondingAuthor":true,"prefix":"","firstName":"Hang","middleName":"","lastName":"Yu","suffix":""},{"id":322239516,"identity":"d8500bab-e9c7-4afb-acad-8a8259f71868","order_by":1,"name":"Haibo Wang","email":"","orcid":"","institution":"Geophysical Survey Team of Anhui Province Bureau of Coal Geology","correspondingAuthor":false,"prefix":"","firstName":"Haibo","middleName":"","lastName":"Wang","suffix":""},{"id":322239517,"identity":"c1c78c7a-d596-4cb8-a882-4d8ae21d8043","order_by":2,"name":"Leibing Wu","email":"","orcid":"","institution":"Geophysical Survey Team of Anhui Province Bureau of Coal Geology","correspondingAuthor":false,"prefix":"","firstName":"Leibing","middleName":"","lastName":"Wu","suffix":""},{"id":322239518,"identity":"39e4bf56-1585-481e-9d0c-0ff1b1793015","order_by":3,"name":"Tongjun Chen","email":"","orcid":"https://orcid.org/0000-0003-2074-4034","institution":"China University of Mining and Technology","correspondingAuthor":false,"prefix":"","firstName":"Tongjun","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-05-17 01:09:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4433498/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4433498/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11600-025-01592-8","type":"published","date":"2025-05-15T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83067921,"identity":"47d3edf1-2045-4a24-87b1-db671b1eef43","added_by":"auto","created_at":"2025-05-19 16:08:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1623765,"visible":true,"origin":"","legend":"","description":"","filename":"actageophysica2024516.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4433498/v1_covered_15b42ab4-8e8c-4329-a642-5f71d779c2d4.pdf"}],"financialInterests":"","formattedTitle":"A Precise Time-Depth Conversion Method for Coal Seam Based on Machine Learning and Seismic Velocity Inversion","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Time-depth conversion, Coal seam, Average velocity, Seismic velocity inversion, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-4433498/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4433498/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTime-depth conversion is a crucial step in 3D seismic interpretation of coalfields. Fast and accurate time-depth conversion is essential for ensuring safe and efficient coal production. However, conventional methods often struggle to balance accuracy with convenience, which makes it difficult to achieve good application results in the coalfield. To address this problem, we proposed a new coal seam time-depth conversion method based on machine learning and seismic velocity inversion. Firstly, a high-precision time-domain layer of the coal seam floor was obtained. Subsequently, the average velocity of the coal seam floor was calculated from boreholes. Following this, post-stack seismic inversion was performed to obtain velocity volumes, and the velocity volumes were subjected to median filtering. Next, machine learning models were trained using the average velocity of the coal seam floor extracted from inverted velocity volume, the average velocity of the coal seam floor calculated and interpolated by control boreholes, and two-way travel times (TWTs) of the coal seam floor as inputs, with actual coal seam floor elevations as the outputs. Finally, different machine learning methods and conventional methods were compared and analyzed for time-depth conversion in coalfield. The results indicate that the Bayesian-SVR model achieved the highest accuracy in time-depth conversion, with a maximum absolute error of only 1.11 meters and a mean absolute error of 0.53 meters at verification boreholes. In summary, this study introduces a machine learning-based coal seam time-depth conversion method that does not require complex velocity models, enhancing efficiency while maintaining high accuracy, which holds significant importance for advancing intelligent coal mining and achieving transparent working faces.\u003c/p\u003e","manuscriptTitle":"A Precise Time-Depth Conversion Method for Coal Seam Based on Machine Learning and Seismic Velocity Inversion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-26 18:39:36","doi":"10.21203/rs.3.rs-4433498/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-11-01T06:43:09+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-07-03T13:20:03+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-03T12:53:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Acta Geophysica","date":"2024-06-19T10:48:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-26T07:28:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Acta Geophysica","date":"2024-05-19T22:52:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b606f503-5a54-4cf0-a7eb-5371e12f87cc","owner":[],"postedDate":"July 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T16:03:56+00:00","versionOfRecord":{"articleIdentity":"rs-4433498","link":"https://doi.org/10.1007/s11600-025-01592-8","journal":{"identity":"acta-geophysica","isVorOnly":false,"title":"Acta Geophysica"},"publishedOn":"2025-05-15 15:57:36","publishedOnDateReadable":"May 15th, 2025"},"versionCreatedAt":"2024-07-26 18:39:36","video":"","vorDoi":"10.1007/s11600-025-01592-8","vorDoiUrl":"https://doi.org/10.1007/s11600-025-01592-8","workflowStages":[]},"version":"v1","identity":"rs-4433498","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4433498","identity":"rs-4433498","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}