Identification of uranium ore deposit type by rare earth elemental composition with multiclass classification using LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis) and KNN (K-Nearest Neighbours) pattern recognition techniques

Identification of uranium ore deposit type by rare earth elemental composition with multiclass classification using LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis) and KNN (K-Nearest Neighbours) pattern recognition techniques | 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 Identification of uranium ore deposit type by rare earth elemental composition with multiclass classification using LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis) and KNN (K-Nearest Neighbours) pattern recognition techniques Mohammad Wasim, Richard G. Brereton This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7367908/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 Rare earth elements (REE) are considered to contain signatures for the provenance of uranium-bearing materials. Identifying the type of uranium deposit is an important part of nuclear forensic investigation. The identification involves matching the REE pattern of an intercepted material with a reference database. Since comparison databases are large, the matching requires multivariate data analysis techniques. In this paper, three pattern recognition techniques have been applied for the selection of a suitable approach for this purpose. These techniques include K-Nearest Neighbors, Linear Discriminant Analysis and Quadratic Discriminant Analysis. The models have been validated using bootstrap training and test splits. The reference database of the current study contains 497 samples covering 5 deposit types (Granite, Intrusive, Metamorphite, Sandstone-Roll Front and Unconformity-related) from 20 countries. Results have been evaluated using the sensitivity, specificity and likelihood ratios. Physical sciences/Engineering Physical sciences/Mathematics and computing Earth and environmental sciences/Solid earth sciences Nuclear Forensics Rare Earth Elements Sensitivity Specificity Likelihood ratio K Nearest Neighbours Linear Discriminant Analysis Quadratic Discriminant Analysis Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarydata.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. 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