首页|Studies from China University of Geosciences Further Understanding of Machine Le arning (Application of Machine Learning to Characterize Metallogenic Potential B ased on Trace Elements of Zircon: A Case Study of the Tethyan Domain)
Studies from China University of Geosciences Further Understanding of Machine Le arning (Application of Machine Learning to Characterize Metallogenic Potential B ased on Trace Elements of Zircon: A Case Study of the Tethyan Domain)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news originating from Beijing, Peop le's Republic of China, by NewsRx correspondents, research stated, "Amidst the r apid advancement of artificial intelligence and information technology, the emer gence of big data and machine learning provides a new research paradigm for mine ral exploration." Funders for this research include National Key R&D Program of China ; National Natural Science Foundation of China; Fundamental Research Funds For T he Central Universities. Our news reporters obtained a quote from the research from China University of G eosciences: "Focusing on the Tethyan metallogenic domain, this paper conducted a series of research works based on machine learning methods to explore the criti cal geochemical element signals that affect the metallogenic potential of porphy ry deposits and reveal the metallogenic regularity. Binary classifiers based on random forest, XGBoost, and deep neural network are established to distinguish z ircon fertility, and these machine learning methods achieve higher accuracy, exc eeding 90%, compared with the traditional geochemical methods. Base d on the random forest and SHapley Additive exPlanations (SHAP) algorithms, key chemical element characteristics conducive to magmatic mineralization are reveal ed."
China University of GeosciencesBeijingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Lear ning
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