Seismic Facies classification and TOC prediction using seismic attributes and Hjorth parameters, in the Groningen field in northeastern Netherlands

Seismic Facies classification and TOC prediction using seismic attributes and Hjorth parameters, in the Groningen field in northeastern Netherlands | 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 Seismic Facies classification and TOC prediction using seismic attributes and Hjorth parameters, in the Groningen field in northeastern Netherlands Reda Al Hasan, Mohammad Hossein Saberi, Mohammad Ali Riahi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6654083/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The use of seismic attributes for characterization and exploration is crucial. This significance increases with the growing number of attributes being used, developed, or newly derived, regardless of their relation to previous ones. Facies classification heavily depends on these attributes in conjunction with other predictive processes. A key focus of these processes is predicting organic carbon content using seismic data, particularly through seismic attributes. The study of organic carbon content has gained attention from researchers because it provides critical information for identifying source rocks and kerogen-bearing formations, which are vital for future drilling operations. In this paper, we employed well-known seismic attributes alongside Hjorth parameters—originally developed for analyzing time-series data in medical applications like EEG signal processing—as innovative seismic attributes for prediction and classification tasks. The most significant of these Hjorth parameters include activity, mobility, and complexity. We applied these parameters through machine learning models, primarily using random forests. The results from integrating these parameters with machine learning models were impressive, achieving a prediction and classification accuracy of up to 93%. Furthermore, this approach provided more accurate information than analyses performed without these parameters. Seismic Attributes Hjorth Parameters Facies Total Organic Carbon Groningen Field Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Jun, 2025 Reviews received at journal 28 Jun, 2025 Reviews received at journal 21 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers invited by journal 27 May, 2025 Editor assigned by journal 27 May, 2025 Editor invited by journal 26 May, 2025 Submission checks completed at journal 26 May, 2025 First submitted to journal 26 May, 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. 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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-6654083","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":462766294,"identity":"96b095b4-130b-4c37-b342-75b8a0f77fb5","order_by":0,"name":"Reda Al Hasan","email":"","orcid":"","institution":"Semnan University","correspondingAuthor":false,"prefix":"","firstName":"Reda","middleName":"Al","lastName":"Hasan","suffix":""},{"id":462766297,"identity":"67238b3a-85b6-49db-bf1e-2e99e2de8030","order_by":1,"name":"Mohammad Hossein Saberi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACCSBmbGBgYGNvQBJNIEoLzwFStTBI4FWGBHRnNzB/+LnDJp9P8vnDRzd32OUxsB9+wPBwD24tZncOsEn2nkmzbJPOMTbOPZNczMCTZsCQ8AyPlhsJbAy8bYcN2KRz2KRz25gTGxhygH45gFcL88e/bf8N2CSPP/+d21af2MD/hqAWBmnetgMGbBIMZsy5bYcTGyQI2pLYJi17JtmAjSfHGOiw44ltEs8MDuDXknz449sddgby7ccffs5tq07s509++PAHHi3QSEECbECMV8MoGAWjYBSMAsIAAJe3T6ag+BpFAAAAAElFTkSuQmCC","orcid":"","institution":"Semnan University","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Hossein","lastName":"Saberi","suffix":""},{"id":462766298,"identity":"ee51f1c0-87a9-4b9d-acf5-a8728d84e233","order_by":2,"name":"Mohammad Ali Riahi","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Ali","lastName":"Riahi","suffix":""}],"badges":[],"createdAt":"2025-05-13 09:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6654083/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6654083/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83687180,"identity":"002593d3-e443-4605-896a-611194589f32","added_by":"auto","created_at":"2025-05-30 18:48:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3080173,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptAlhasanversion3forDiscoverAppliedSciences.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6654083/v1_covered_9d8206ef-02cb-4b55-9d19-91b0a1e47db1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seismic Facies classification and TOC prediction using seismic attributes and Hjorth parameters, in the Groningen field in northeastern Netherlands","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":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Seismic Attributes, Hjorth Parameters, Facies, Total Organic Carbon, Groningen Field","lastPublishedDoi":"10.21203/rs.3.rs-6654083/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6654083/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe use of seismic attributes for characterization and exploration is crucial. 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