Petrographic, mineralogical and structural characterization of Nkout center iron deposit (south Cameroon) and geological mapping using k-NN machine learning algorithm

Petrographic, mineralogical and structural characterization of Nkout center iron deposit (south Cameroon) and geological mapping using k-NN machine learning algorithm | 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 Petrographic, mineralogical and structural characterization of Nkout center iron deposit (south Cameroon) and geological mapping using k-NN machine learning algorithm André William BOROH, Amadou DIGUIM KEPMANOU, Alpha Baster KENFACK FOKEM, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7632063/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract In the process of exploring potential resources, estimation methods are crucial instruments. The identification of lithological layers in the field of geological mapping heavily depends on how well the prediction techniques work. In this work, we use artificial intelligence specifically, machine learning and its k nearest neighbour algorithm to establish a novel paradigm of geological mapping. A petrographic and mineralogical characterization of the non-mineralized deposits was done as the initial step in this investigation. The presence of plutonic rocks such amphiboles, amphibole and biotite granites, amphibole and biotite gneisses, and amphibolite’s was disclosed by these formations. Gneisses and amphibolite, which are all rich in magnetite, are the mineralized formations. Banded iron formations, such as coarse banded BIF (CMB), hematite magnetite BIF (HMB), and magnetite hematite BIF (MHB), are subsequently formed. It was possible to ascertain the type and traits of the various structures in the research region thanks to the structural analysis, which was performed for ductile and brittle deformation. The superficial layers produced by the logging of the boreholes served as the basis for the geological mapping by k-NN. It was feasible to acquire the hyperparameters, in particular on the number of k = 3 with the Euclidian metric distance, thanks to model optimization. This model, which was used to create the final geological map, demonstrated performance with an average confusion matrix performance of 86%, a correlation coefficient R² of 91%, and an AUC for the ROC curve of 0.99. Geological mapping machine learning nearest neighbor algorithm Nkout iron deposit Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviews received at journal 20 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 13 Oct, 2025 Editor invited by journal 09 Oct, 2025 Editor assigned by journal 27 Sep, 2025 Submission checks completed at journal 27 Sep, 2025 First submitted to journal 16 Sep, 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-7632063","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533806216,"identity":"033fa047-ff0b-4743-aa31-93b44b7072b6","order_by":0,"name":"André William BOROH","email":"","orcid":"","institution":"University of Ngaoundere","correspondingAuthor":false,"prefix":"","firstName":"André","middleName":"William","lastName":"BOROH","suffix":""},{"id":533806219,"identity":"20ececdc-6257-41ef-90df-33b171a91591","order_by":1,"name":"Amadou DIGUIM 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The identification of lithological layers in the field of geological mapping heavily depends on how well the prediction techniques work. In this work, we use artificial intelligence specifically, machine learning and its k nearest neighbour algorithm to establish a novel paradigm of geological mapping. A petrographic and mineralogical characterization of the non-mineralized deposits was done as the initial step in this investigation. The presence of plutonic rocks such amphiboles, amphibole and biotite granites, amphibole and biotite gneisses, and amphibolite\u0026rsquo;s was disclosed by these formations. Gneisses and amphibolite, which are all rich in magnetite, are the mineralized formations. Banded iron formations, such as coarse banded BIF (CMB), hematite magnetite BIF (HMB), and magnetite hematite BIF (MHB), are subsequently formed. It was possible to ascertain the type and traits of the various structures in the research region thanks to the structural analysis, which was performed for ductile and brittle deformation. The superficial layers produced by the logging of the boreholes served as the basis for the geological mapping by k-NN. It was feasible to acquire the hyperparameters, in particular on the number of k\u0026thinsp;=\u0026thinsp;3 with the Euclidian metric distance, thanks to model optimization. This model, which was used to create the final geological map, demonstrated performance with an average confusion matrix performance of 86%, a correlation coefficient R\u0026sup2; of 91%, and an AUC for the ROC curve of 0.99.\u003c/p\u003e","manuscriptTitle":"Petrographic, mineralogical and structural characterization of Nkout center iron deposit (south Cameroon) and geological mapping using k-NN machine learning algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 13:21:23","doi":"10.21203/rs.3.rs-7632063/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-05T11:52:17+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"74272036037127602031273241402427478388","date":"2025-11-04T07:19:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T07:15:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T08:45:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309907270157236409863058054252232114419","date":"2025-10-20T06:20:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121426183297556596665067054332826126040","date":"2025-10-17T09:14:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74272036037127602031273241402427478388","date":"2025-10-14T06:12:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T07:46:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-09T16:50:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T07:15:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-27T07:14:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2025-09-16T15:12:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09ad8805-c24e-446f-9a38-d107ba17fa99","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T05:53:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 13:21:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7632063","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7632063","identity":"rs-7632063","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}