Uncertainty Analysis of Deep Learning-Based Geochemical Models Using a New Approach (Southeast Jiroft, Kerman)

Uncertainty Analysis of Deep Learning-Based Geochemical Models Using a New Approach (Southeast Jiroft, Kerman) | 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 Uncertainty Analysis of Deep Learning-Based Geochemical Models Using a New Approach (Southeast Jiroft, Kerman) Farkhondeh Khademi, Dariush Esmaeily, Ali Kananian, Zohre Hoseinzade This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8868744/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 Identifying geochemical anomalies is key in mineral exploration and requires the application of advanced analytical methods due to the complexity of spatial data and the presence of multiple sources of uncertainty. In this study, the performance of five algorithms, including two classical methods (SOM and BSOM) and three deep learning algorithms (Embedded Autoencoder, Deep Embedded Clustering, and LSTM) was evaluated in detecting geochemical anomalies within a portion of the Urumieh-Dokhtar magmatic belt. The results indicated that each algorithm exhibits its unique performance and demonstrates relative efficiency under different conditions. Subsequently, to leverage the relative advantages of each model and reduce uncertainty, a hybrid model based on the Maximum Likelihood Estimation (MLE) method was designed and implemented. The hybrid model achieved superior performance compared to any individual algorithm by integrating the most frequent labels among the models. The validity of the obtained results was confirmed through field visits and microscopic studies of the collected samples, which revealed that valuable elements such as gold, copper, and arsenic were significantly enriched in the areas proposed by the models. Overall, the findings of this research highlight the superiority of deep learning methods, particularly the AESOM algorithm, in anomaly detection. It is recommended to use hybrid models as an effective strategy for managing exploration uncertainties. Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Earth and environmental sciences/Solid earth sciences Maximum likelihood estimation self-organizing neural network deep embedded clustering deep embedded autoencoder recurrent neural network geochemical anomaly uncertainty. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviews received at journal 08 May, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers invited by journal 26 Feb, 2026 Editor assigned by journal 26 Feb, 2026 Editor invited by journal 26 Feb, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 25 Feb, 2026 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-8868744","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":597828288,"identity":"fbd13c39-45f7-45d1-9fe5-d1c1976c705f","order_by":0,"name":"Farkhondeh Khademi","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Farkhondeh","middleName":"","lastName":"Khademi","suffix":""},{"id":597828289,"identity":"7c7d6cd7-495d-4fe6-aea4-86bac36523be","order_by":1,"name":"Dariush Esmaeily","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYDCCAyDCwIKHH8rnIVaLBI9kAzNJWhgkGAwOMBPpLr7zpxM/fCiQkDG+kX+A4UcNg4x5AwEtkjdyN0vOADrM7EYyA2PPMQYemQMEtBjc4N0gzQPVwsDbwMAjQchhBufPbv79B6jFeAbQlr9EaTmQu00aFGIGEskMzETZAvTLNsseoBaJM48NDssckyCshQ/osBs//tjY87cnPnz4psbGnqAWFHAAFEGjYBSMglEwCqgAAD+5Nrl2nDNkAAAAAElFTkSuQmCC","orcid":"","institution":"University of Tehran","correspondingAuthor":true,"prefix":"","firstName":"Dariush","middleName":"","lastName":"Esmaeily","suffix":""},{"id":597828290,"identity":"781893ce-74c8-4b67-a050-74956d1b11f3","order_by":2,"name":"Ali Kananian","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Kananian","suffix":""},{"id":597828291,"identity":"6863b0a9-c92e-4b3d-9016-c53e05cc7e73","order_by":3,"name":"Zohre Hoseinzade","email":"","orcid":"","institution":"Hamedan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zohre","middleName":"","lastName":"Hoseinzade","suffix":""}],"badges":[],"createdAt":"2026-02-13 07:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8868744/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8868744/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104401096,"identity":"21c3a4cf-a2da-451e-9dff-8534f226fa69","added_by":"auto","created_at":"2026-03-11 12:11:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2459922,"visible":true,"origin":"","legend":"","description":"","filename":"khademi.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8868744/v1_covered_c5374a62-6f52-4178-a430-2d1c0a8fcdb6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncertainty Analysis of Deep Learning-Based Geochemical Models Using a New Approach (Southeast Jiroft, Kerman)","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"Maximum likelihood estimation, self-organizing neural network, deep embedded clustering, deep embedded autoencoder, recurrent neural network, geochemical anomaly, uncertainty.","lastPublishedDoi":"10.21203/rs.3.rs-8868744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8868744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIdentifying geochemical anomalies is key in mineral exploration and requires the application of advanced analytical methods due to the complexity of spatial data and the presence of multiple sources of uncertainty. 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