Comparative evaluation of MLFN and RBFN in integrated seismic inversion: physics-guided pseudo-well augmentation for 3D acoustic impedance modeling in an offshore clastic field, southwest Iran

Comparative evaluation of MLFN and RBFN in integrated seismic inversion: physics-guided pseudo-well augmentation for 3D acoustic impedance modeling in an offshore clastic field, southwest Iran | 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 Article Comparative evaluation of MLFN and RBFN in integrated seismic inversion: physics-guided pseudo-well augmentation for 3D acoustic impedance modeling in an offshore clastic field, southwest Iran Arash Ghiasvand, Abdolrahim Javaherian, Maryam Amirmazlaghani, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8003709/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Accurate three-dimensional acoustic impedance modeling in offshore clastic reservoirs remains a significant challenge due to sparse well control and the highly nonlinear relationship between seismic attributes and subsurface elastic properties. This study introduces an integrated, physics-guided machine learning (ML) workflow that combines rock-physics-driven pseudo-well generation with neural networks to directly map seismic attributes to acoustic impedance under data-limited conditions. A soft-sand rock physics workflow was applied, in which grain moduli were determined using the Voigt–Reuss–Hill average. The dry rock frame was modeled at critical porosity by Hertz–Mindlin contact theory and then interpolated toward zero porosity using the Modified Hashin–Shtrikman lower bound. Gassmann fluid substitution was subsequently performed. Using this approach, 45 pseudo-wells were generated and conditioned through lithofacies classification and spatial statistics, mitigating the risk of overfitting associated with the three available real wells. Six seismic attributes—envelope, RMS amplitude, instantaneous phase, instantaneous frequency, quadrature trace, and sweetness—were selected as predictors. Two neural architectures, a multi-layer feedforward network (MLFN) and a radial basis function network (RBFN), were trained and benchmarked using a leave-one-well-out cross-validation scheme. The MLFN achieved higher predictive accuracy (CC = 0.87, NRMSE = 0.493) compared to the RBFN (CC = 0.79, NRMSE = 0.613), which may reflect its greater capacity to model broader hierarchical relationships between seismic attributes and acoustic impedance. The resulting impedance volume delineates laterally coherent high-impedance sandstone units and low-impedance porous intervals consistent with geological interpretation. These results suggest that integrating physics-guided pseudo-well augmentation with feed-forward neural networks offers a practical and computationally efficient approach for acoustic impedance inversion in data-limited offshore settings. Future work may explore validation across diverse geological settings to assess the robustness and transferability of the proposed methodology. This study provides a basis for hybrid and uncertainty-aware inversion frameworks that may help address complexities in heterogeneous reservoir systems, highlighting the importance of reproducible and widely applicable data-driven seismic inversion methods under sparse well control. Physical sciences/Engineering Earth and environmental sciences/Solid earth sciences seismic inversion acoustic impedance pseudo-well generation MLFN RBFN rock physics modeling Full Text Additional Declarations No competing interests reported. Supplementary Files GhiasvandetalGraphicalabstract01N0v25.docx GhiasvandetalHighlights01Nov25.docx GhiasvandetalListofsymbols01Nov25.docx Cite Share Download PDF Status: Published Journal Publication published 02 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviews received at journal 08 Dec, 2025 Reviews received at journal 03 Dec, 2025 Reviewers agreed at journal 28 Nov, 2025 Reviewers agreed at journal 28 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviews received at journal 20 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers invited by journal 12 Nov, 2025 Editor assigned by journal 12 Nov, 2025 Editor invited by journal 11 Nov, 2025 Submission checks completed at journal 07 Nov, 2025 First submitted to journal 07 Nov, 2025 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-8003709","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":549324124,"identity":"1f67dd1b-2b44-43f0-98dd-84617789c2a6","order_by":0,"name":"Arash Ghiasvand","email":"","orcid":"","institution":"Amirkabir University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Arash","middleName":"","lastName":"Ghiasvand","suffix":""},{"id":549324125,"identity":"ba0d6df7-fbfd-4522-a589-898bd1623324","order_by":1,"name":"Abdolrahim 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Iran","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":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"seismic inversion, acoustic impedance, pseudo-well generation, MLFN, RBFN, rock physics modeling","lastPublishedDoi":"10.21203/rs.3.rs-8003709/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8003709/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate three-dimensional acoustic impedance modeling in offshore clastic reservoirs remains a significant challenge due to sparse well control and the highly nonlinear relationship between seismic attributes and subsurface elastic properties. This study introduces an integrated, physics-guided machine learning (ML) workflow that combines rock-physics-driven pseudo-well generation with neural networks to directly map seismic attributes to acoustic impedance under data-limited conditions. A soft-sand rock physics workflow was applied, in which grain moduli were determined using the Voigt\u0026ndash;Reuss\u0026ndash;Hill average. The dry rock frame was modeled at critical porosity by Hertz\u0026ndash;Mindlin contact theory and then interpolated toward zero porosity using the Modified Hashin\u0026ndash;Shtrikman lower bound. Gassmann fluid substitution was subsequently performed. Using this approach, 45 pseudo-wells were generated and conditioned through lithofacies classification and spatial statistics, mitigating the risk of overfitting associated with the three available real wells. Six seismic attributes\u0026mdash;envelope, RMS amplitude, instantaneous phase, instantaneous frequency, quadrature trace, and sweetness\u0026mdash;were selected as predictors. Two neural architectures, a multi-layer feedforward network (MLFN) and a radial basis function network (RBFN), were trained and benchmarked using a leave-one-well-out cross-validation scheme. The MLFN achieved higher predictive accuracy (CC\u0026thinsp;=\u0026thinsp;0.87, NRMSE\u0026thinsp;=\u0026thinsp;0.493) compared to the RBFN (CC\u0026thinsp;=\u0026thinsp;0.79, NRMSE\u0026thinsp;=\u0026thinsp;0.613), which may reflect its greater capacity to model broader hierarchical relationships between seismic attributes and acoustic impedance. The resulting impedance volume delineates laterally coherent high-impedance sandstone units and low-impedance porous intervals consistent with geological interpretation. These results suggest that integrating physics-guided pseudo-well augmentation with feed-forward neural networks offers a practical and computationally efficient approach for acoustic impedance inversion in data-limited offshore settings. Future work may explore validation across diverse geological settings to assess the robustness and transferability of the proposed methodology. This study provides a basis for hybrid and uncertainty-aware inversion frameworks that may help address complexities in heterogeneous reservoir systems, highlighting the importance of reproducible and widely applicable data-driven seismic inversion methods under sparse well control.\u003c/p\u003e","manuscriptTitle":"Comparative evaluation of MLFN and RBFN in integrated seismic inversion: physics-guided pseudo-well augmentation for 3D acoustic impedance modeling in an offshore clastic field, southwest Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 16:09:04","doi":"10.21203/rs.3.rs-8003709/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-10T04:21:34+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"173434202915797356887853350899422937852","date":"2025-12-08T14:24:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T13:57:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T06:30:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106022573266033113623361815382510271582","date":"2025-11-28T09:14:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175429464721466556402298162364894663781","date":"2025-11-28T06:45:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1610629923048624228090720513238481711","date":"2025-11-26T02:20:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T12:44:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237445476651458842090791968669110891193","date":"2025-11-14T08:14:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-12T05:59:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-12T05:50:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-11T10:28:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-07T13:07:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-07T13:02:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2f513787-d3b0-4ae7-b234-b4c5b8b36dbf","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58450443,"name":"Physical sciences/Engineering"},{"id":58450444,"name":"Earth and environmental sciences/Solid earth sciences"}],"tags":[],"updatedAt":"2026-01-05T16:03:07+00:00","versionOfRecord":{"articleIdentity":"rs-8003709","link":"https://doi.org/10.1038/s41598-025-34327-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-02 15:57:07","publishedOnDateReadable":"January 2nd, 2026"},"versionCreatedAt":"2025-11-25 16:09:04","video":"","vorDoi":"10.1038/s41598-025-34327-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-34327-2","workflowStages":[]},"version":"v1","identity":"rs-8003709","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8003709","identity":"rs-8003709","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}