[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126314-en":3,"doc-seo-126314-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126314,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Discrimination of deposit types using magnetite geochemistry based on machine learning","Trace elements in magnetite are important for deposit genesis research and mineral exploration, yet traditional low-dimensional analysis cannot effectively reveal genetic deposit types from magnetite chemistry. This study uses supervised machine learning, eXtreme Gradient Boosting (XGBoost), to link multi-element magnetite compositions to deposit types. Using 3,865 magnetite trace-element datasets from six deposit categories, the model reaches 96% overall accuracy and a 95% F1 score, with SHAP identifying Ni, Ga, Sc, and V as key discriminative elements. A combined XGBoost–t-SNE visualization is proposed, and application to the Jinchuan Ni-Cu-PGE system classifies magnetite into three types, where type II suggests interaction between sulfide melts and volatile fluids.","Montclair State University  \nMontclair State University Digital Commons  \nDepartment of Earth and Environmental Studies Faculty Scholarship and Creative Works  \nDepartment of Earth and Environmental Studies  \n7-1-2024  \nDiscrimination of deposit types using magnetite geochemistry based on machine learning  \nPeng Wang  \nSchool of the Earth Sciences and Resources  \nShang Guo Su  \nSchool of the Earth Sciences and Resources  \nGuan Zhi Wang  \nSchool of the Earth Sciences and Resources  \nYang Yang Dong  \nSEPCO Electric Power Construction Corp  \nDanlin Yu  \nMontclair State University, [yud@montclair.edu](yud@montclair.edu)  \nFollow this and additional works at: [https://digitalcommons.montclair.edu/earth-environ-studies-facpubs](https://digitalcommons.montclair.edu/earth-environ-studies-facpubs)  \n Part of the Earth Sciences Commons, and the Environmental Sciences Commons  \nMSU Digital Commons Citation  \nWang, Peng; Su, Shang Guo; Wang, Guan Zhi; Dong, Yang Yang; and Yu, Danlin, \"Discrimination of deposit types using magnetite geochemistry based on machine learning\" (2024) . Department of Earth and Environmental Studies Faculty Scholarship and Creative Works. 767.  \n[https://digitalcommons.montclair.edu/earth-environ-studies-facpubs/767](https://digitalcommons.montclair.edu/earth-environ-studies-facpubs/767)  \nThis Article is brought to you for free and open access by the Department of Earth and Environmental Studies at Montclair State University Digital Commons. It has been accepted for inclusion in Department of Earth and Environmental Studies Faculty Scholarship and Creative Works by an authorized administrator of Montclair State University Digital Commons. For more information, please contact [digitalcommons@montclair.edu](digitalcommons@montclair.edu).  \nOre Geology Reviews 170 (2024) 106107  \nContents lists available at ScienceDirect  \nOre Geology Reviews  \njournal [homepage:](homepage: www.elsevier.com/locate/oregeorev)[ www.elsevier.com/locate/oregeorev](homepage: www.elsevier.com/locate/oregeorev)  \n| Discrimination of deposit types using magnetite geochemistry based on machine learning |  |  |  |\n| --- | --- | --- | --- |\n| Peng Wang a , Shang-Guo Sua, * , Guan-Zhi Wang a , Yang-Yang Dong b , Dan-lin Yu c\u003Cbr>a School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China b SEPCO Electric Power Construction Corp, Jinan 250102, China\u003Cbr>c Center for Environmental and Life Sciences, Montclair State University, Montclair, NJ 07043, USA |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords: Machine learning Magnetite Trace elements XGBoost\u003Cbr>t-SNE |  | The application of trace elements in magnetite for deposit genesis research is significant, highlighting its potential as a valuable mineral exploration tool. However, traditional low-dimensional analysis methods are not effective in revealing the genetic types of deposits using magnetite trace elements, as they fail to fully utilize the rich high-dimensional information provided by magnetite trace element analysis. To address this limitation, we implemented a supervised machine learning method, eXtreme Gradient Boosting (XGBoost), to correlate the multi-element composition of magnetite with deposit types. Our study encompassed 3,865 magnetite trace element datasets from six distinct deposit types (BIF, IOA, IOCG, magmatic, porphyry, and skarn deposits). It demonstrates the XGBoost classifier’s efficiency and accuracy in classifying high-dimensional magnetite trace element data based on deposit types, achieving an impressive overall accuracy of 96% with an F1 score of 95%. Interpretation of the model using the SHAPley Additive exPlanations (SHAP) tool shows that Ni, Ga, Sc, and V are the most indicative elements for classifying deposit types using magnetite trace element chemistry. Additionally, a visualization method based on XGBoost and t-SNE was proposed. Finally, Xgboost and SHAP were applied in Jinchuan magmatic sulfide Ni-C","cbCaiqWs6lculuEQ","https://ap.wps.com/l/cbCaiqWs6lculuEQ","pdf",18461300,6,1,19,"English","en",105,"# Introduction\n## Magnetite trace elements as indicators of deposit types\n## Limitations of traditional low-dimensional methods\n## Study approach with supervised machine learning","[{\"question\":\"Why are magnetite trace elements important in deposit genesis research?\",\"answer\":\"Magnetite trace-element chemistry records geological formation conditions, so variations help distinguish deposit genetic types and support mineral exploration.\"},{\"question\":\"How does the study discriminate deposit types using magnetite data?\",\"answer\":\"It applies a supervised XGBoost classifier to correlate multi-element magnetite composition with six deposit categories.\"},{\"question\":\"Which elements are most influential for the model’s classification?\",\"answer\":\"SHAP analysis indicates Ni, Ga, Sc, and V are the most indicative elements for distinguishing deposit types.\"}]","Discrimination of deposit types using magnetite geochemistry based on machine learning | PDF",1785904409,48,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"discrimination-of-deposit-types-using-magnetite-geochemistry-based-on-machine-learning","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/discrimination-of-deposit-types-using-magnetite-geochemistry-based-on-machine-learning/126314/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why are magnetite trace elements important in deposit genesis research?","Question",{"text":77,"@type":78},"Magnetite trace-element chemistry records geological formation conditions, so variations help distinguish deposit genetic types and support mineral exploration.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the study discriminate deposit types using magnetite data?",{"text":82,"@type":78},"It applies a supervised XGBoost classifier to correlate multi-element magnetite composition with six deposit categories.",{"name":84,"@type":75,"acceptedAnswer":85},"Which elements are most influential for the model’s classification?",{"text":86,"@type":78},"SHAP analysis indicates Ni, Ga, Sc, and V are the most indicative elements for distinguishing deposit types.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":108,"slug":138},"General","general"]