Concerning traditional models and machine learning approaches in geochemical data processing

Concerning traditional models and machine learning approaches in geochemical data processing | 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 Concerning traditional models and machine learning approaches in geochemical data processing Monir Modjarrad, Hadi Mostafavi Amjad, Mahrokh G. Shayesteh, Amin Danandeh Hesar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6401121/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Recent advances in machine learning (ML) highlight significant improvements in judgment capabilities, particularly in geochemical sciences and environmental research. This study underscores the evolution from traditional geoscientific models to modern data-driven approaches. It introduces an application of ML methods including unsupervised learning and dimensionality reduction to identify geochemical characteristics in mineral compositions. Integrating mineral chemistry analysis with ML addresses inefficiencies in processing large datasets generated by electron probe micro-analyzers (EPMA). We introduce a new algorithm for automating mineral chemistry data processing, achieving approximately 90% accuracy in classifying minerals from an extensive dataset of ultramafic rocks. Peridotites are believed to form from the partial melting of the Earth's mantle and represent the residues left after the extraction of basaltic melts during mantle upwelling. Abyssal (oceanic lithosphere) and fore-arc (subduction zone) peridotites provide valuable insights into the composition and dynamics of the upper mantle, including melt generation and mantle convection processes, interactions between tectonic plates, and the formation of new oceanic crust. Discriminating between abyssal and fore-arc peridotite tectonic settings using geochemical diagrams has been challenge due to significant data overlap. Initial analyses with established frameworks based on various geochemical parameters yielded inconclusive results. However, a new method focusing on mono-mineral compositions of spinel, olivine, and pyroxenes—validated with data from 600 samples across multiple ophiolitic/oceanic locations—can discriminate the abyssal and fore-arc settings more successfully than the traditional methods. Therefore, ML techniques have been employed to enhance discrimination between the two settings for new investigations and/or for previously well-studied peridotites. ML applications Geosciences Mineral chemistry Peridotites Abyssal Fore-arc Full Text Additional Declarations No competing interests reported. Supplementary Files SupplTableS1Dataset1.xlsx SupplTableS2Dataset2.xlsx SupplTableS3Dataset3.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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-6401121","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458302572,"identity":"3261c4c9-784b-4776-b7cc-10f0ad2a1603","order_by":0,"name":"Monir Modjarrad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYLACHh4wZfgAxIYIGRCnxdiABC0QykyCKDfpNnAnPngjY5dv3n54WzXvnm0y/A3MDz8wFNzDqcXsAO9mwzk8yZZzzqSV3eZ5dptH4gCbsQSDQTE+LdukeXiYDSQYcsxu8xy4zcNwgMEM6JcEQlrqDST435gVg7TIH2D/RoyWwwYSEjlmzCAtBgd4CNhyGOyX40Atz4ol5wC1GB7mKZZIwKfleO/GB297qoEOS9744c2B2/Zyx9s3fvjwB7cWBmYgZuxBF8GjAQp+EFQxCkbBKBgFIxkAAHC2SfvRH1lwAAAAAElFTkSuQmCC","orcid":"","institution":"Urmia University","correspondingAuthor":true,"prefix":"","firstName":"Monir","middleName":"","lastName":"Modjarrad","suffix":""},{"id":458302573,"identity":"c35e1970-b262-48ac-bd91-f5e7551ce2fb","order_by":1,"name":"Hadi Mostafavi Amjad","email":"","orcid":"","institution":"Urmia University","correspondingAuthor":false,"prefix":"","firstName":"Hadi","middleName":"Mostafavi","lastName":"Amjad","suffix":""},{"id":458302574,"identity":"0a0b0faa-eaaa-4ab4-a892-813f8b0e3915","order_by":2,"name":"Mahrokh G. Shayesteh","email":"","orcid":"","institution":"Urmia University","correspondingAuthor":false,"prefix":"","firstName":"Mahrokh","middleName":"G.","lastName":"Shayesteh","suffix":""},{"id":458302575,"identity":"8aeaaf4f-5437-48bf-b4d5-ac626e56d104","order_by":3,"name":"Amin Danandeh Hesar","email":"","orcid":"","institution":"Urmia University","correspondingAuthor":false,"prefix":"","firstName":"Amin","middleName":"Danandeh","lastName":"Hesar","suffix":""}],"badges":[],"createdAt":"2025-04-08 08:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6401121/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6401121/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83741168,"identity":"923f08b9-ee9d-4e75-9c7b-54f48db2a8c9","added_by":"auto","created_at":"2025-06-01 23:46:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2059138,"visible":true,"origin":"","legend":"","description":"","filename":"text.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6401121/v1_covered_247a7f84-f0e5-4cab-9a07-58aeba8170a6.pdf"},{"id":83130705,"identity":"fa500d65-cd05-4b42-944c-02c8471ca609","added_by":"auto","created_at":"2025-05-20 10:17:44","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":126776,"visible":true,"origin":"","legend":"","description":"","filename":"SupplTableS1Dataset1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6401121/v1/a5c29fc1efaf469cc59f5c9f.xlsx"},{"id":83130138,"identity":"d6b50eb8-750f-4a09-a52b-d0a0be547446","added_by":"auto","created_at":"2025-05-20 10:09:44","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":58119,"visible":true,"origin":"","legend":"","description":"","filename":"SupplTableS2Dataset2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6401121/v1/8df6c0288552df85c81a76b8.xlsx"},{"id":83130139,"identity":"551f20ab-2225-43b1-a2ff-a896fc7f7589","added_by":"auto","created_at":"2025-05-20 10:09:44","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":91265,"visible":true,"origin":"","legend":"","description":"","filename":"SupplTableS3Dataset3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6401121/v1/5c396a152d2e79eccc4df96a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Concerning traditional models and machine learning approaches in geochemical data processing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ML applications, Geosciences, Mineral chemistry, Peridotites, Abyssal, Fore-arc","lastPublishedDoi":"10.21203/rs.3.rs-6401121/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6401121/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecent advances in machine learning (ML) highlight significant improvements in judgment capabilities, particularly in geochemical sciences and environmental research. This study underscores the evolution from traditional geoscientific models to modern data-driven approaches. It introduces an application of ML methods including unsupervised learning and dimensionality reduction to identify geochemical characteristics in mineral compositions. Integrating mineral chemistry analysis with ML addresses inefficiencies in processing large datasets generated by electron probe micro-analyzers (EPMA). We introduce a new algorithm for automating mineral chemistry data processing, achieving approximately 90% accuracy in classifying minerals from an extensive dataset of ultramafic rocks.\u003c/p\u003e \u003cp\u003ePeridotites are believed to form from the partial melting of the Earth's mantle and represent the residues left after the extraction of basaltic melts during mantle upwelling. Abyssal (oceanic lithosphere) and fore-arc (subduction zone) peridotites provide valuable insights into the composition and dynamics of the upper mantle, including melt generation and mantle convection processes, interactions between tectonic plates, and the formation of new oceanic crust.\u003c/p\u003e \u003cp\u003eDiscriminating between abyssal and fore-arc peridotite tectonic settings using geochemical diagrams has been challenge due to significant data overlap. Initial analyses with established frameworks based on various geochemical parameters yielded inconclusive results. However, a new method focusing on mono-mineral compositions of spinel, olivine, and pyroxenes\u0026mdash;validated with data from 600 samples across multiple ophiolitic/oceanic locations\u0026mdash;can discriminate the abyssal and fore-arc settings more successfully than the traditional methods. Therefore, ML techniques have been employed to enhance discrimination between the two settings for new investigations and/or for previously well-studied peridotites.\u003c/p\u003e","manuscriptTitle":"Concerning traditional models and machine learning approaches in geochemical data processing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 10:09:40","doi":"10.21203/rs.3.rs-6401121/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a2e0c35-e302-44fe-8684-8aa6caf9f70f","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-01T23:38:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-20 10:09:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6401121","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6401121","identity":"rs-6401121","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}