[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123154-en":3,"doc-seo-123154-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123154,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Magnetite Talks - Testing Machine Learning Models to Untangle Ore Deposit Classification - A Case Study in the Ossa-Morena Zone (Portugal, SW Iberia)","A comprehensive investigation evaluates machine learning algorithms for accurately classifying mineral deposit types, targeting iron deposits within Portugal’s Ossa-Morena Zone. Using a limited dataset of magnetite trace-element geochemistry, the study derives methodological and metallogenic insights from model outputs. Results show that combining restricted trace-element magnetite data with multiple ML methods supports reliable classification. Random forest, naïve Bayes, and multinomial logistic regression provide the highest accuracy, while SVM, k-nearest neighbour, and artificial neural networks perform worse. Literature-derived class mappings applied to selected deposits yield confident assignments and reveal cryptic mixed origins, supporting exploration target interpretation.","minerals   \nArticle  \nMagnetite Talks: Testing Machine Learning Models to Untangle Ore Deposit Classification—A Case Study in the Ossa-Morena Zone (Portugal, SW Iberia)  \nPedro Nogueira 1,2, * and Miguel Maia 2  \nCitation: Nogueira, P.; Maia, M.  \nMagnetite Talks: Testing Machine Learning Models to Untangle Ore Deposit Classification—A Case Study in the Ossa-Morena Zone (Portugal, SW Iberia) . Minerals 2023, 13, 1009 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)min13081009  \nAcademic Editor: Paul Alexandre  \nReceived: 28 June 2023  \nRevised: 25 July 2023  \nAccepted: 26 July 2023  \nPublished: 29 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Geosciences, School of Science and Technology, 􀂒vora University, Rua Rom¢o Ramalho 59, 7000-761 􀂒vora, Portugal  \n2 Institute of Earth Sciences–􀂒vora Pole, Rua Rom¢o Ramalho 59, 7000-761 􀂒vora, Portugal; [mcmaiageo@gmail.com](mcmaiageo@gmail.com)  \n* [Correspondence: pmn@uevora.pt](Correspondence: pmn@uevora.pt)  \nAbstract: A comprehensive investigation into the application of machine learning algorithms for accurately classifying mineral deposit types is presented. The study specifically focuses on iron deposits in the Portuguese Ossa-Morena Zone, employing a limited dataset of trace element geochemistry from magnetites. The research aims to derive meaningful methodological and metallogenic conclusions from the obtained results. The findings demonstrate that the combination of a restricted dataset of trace element geochemistry from magnetites with diverse machine learning models serves as a reliable tool for achieving precise classifications of mineral deposit types. Among the machine learning methods evaluated, random forest, naïve Bayes, and multinomial logistic regression emerge as the most accurate classifiers, whereas the support vector machine, the k-nearest neighbour, andartificial neural networks exhibit lower performance scores. By integrating all literature-proposed classifications, and applying them to selected iron deposits, confident classifications were obtained. Alvito and Azenhas are reliably classified as skarns, whereas Monges, Serrinha, and Vale da Arcaare classified as either porphyry or a Banded Iron Formation (BIF) . Notably, the classification of Orada proves cryptic, encompassing both BIF and volcanogenic massive sulphide (VMS) deposit types. Moreover, the application of machine learning models to pertinent case studies offers valuable insights not only for classifying mineral deposit types but also for discerning mixed or complex origins. This approach provides meaningful results that can aid in the interpretation of mineral deposit types and may facilitate the identification of new mineral exploration targets. The research highlights the robustness of machine learning algorithms in interpreting magnetite data and underscores their potential significance in exploration projects.  \nKeywords: mineral deposit classification; trace element geochemistry; magnetites; LA-ICP-MS; Ossa-Morena Zone  \n1. Introduction  \nOne challenging task in the exploration of ore deposits is defining the model that best fits a certain deposit type, based on the combination of multiple sources of data. The accurate definition of such models is a key factor for driving mineral exploration campaigns, helping to define the applicable methodologies and strategies, thus saving time and money. Magnetite is a common mineral present in multiple metallogenic settings, so it is a convenient target to perform such a task. Its trace element composition provides valuable information to constrain the conditions and mechanisms associated with ore deposit","cbCaic7pgqj7GE7z","https://ap.wps.com/l/cbCaic7pgqj7GE7z","pdf",3126395,1,21,"English","en",105,"# Introduction\n## Machine learning for ore deposit classification\n## Magnetite trace-element data and LA-ICP-MS\n## Study objective and approach","[{\"question\":\"What problem does the study address in ore deposit exploration?\",\"answer\":\"It focuses on selecting the best model for defining ore deposit types from multiple data sources, which is crucial for directing exploration efficiently.\"},{\"question\":\"How was the dataset constructed for the machine learning experiments?\",\"answer\":\"The study uses a limited dataset of trace element geochemistry from magnetites measured for iron deposits in the Ossa-Morena Zone.\"},{\"question\":\"Which machine learning models achieved the best classification performance?\",\"answer\":\"Random forest, naïve Bayes, and multinomial logistic regression were reported as the most accurate classifiers, while SVM, k-nearest neighbour, and artificial neural networks showed lower performance.\"}]","Magnetite Talks - Testing Machine Learning Models to Untangle Ore Deposit Classification - A Case Study in the Ossa-Morena Zone (Portugal, SW Iberia) | PDF",1785814943,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"magnetite-talks-testing-machine-learning-models-to-untangle-ore-deposit-classification-a-case-study-in-the-ossa-morena-zone-portugal-sw-iberia","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/magnetite-talks-testing-machine-learning-models-to-untangle-ore-deposit-classification-a-case-study-in-the-ossa-morena-zone-portugal-sw-iberia/123154/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in ore deposit exploration?","Question",{"text":75,"@type":76},"It focuses on selecting the best model for defining ore deposit types from multiple data sources, which is crucial for directing exploration efficiently.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset constructed for the machine learning experiments?",{"text":80,"@type":76},"The study uses a limited dataset of trace element geochemistry from magnetites measured for iron deposits in the Ossa-Morena Zone.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models achieved the best classification performance?",{"text":84,"@type":76},"Random forest, naïve Bayes, and multinomial logistic regression were reported as the most accurate classifiers, while SVM, k-nearest neighbour, and artificial neural networks showed lower performance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]