首页|Researcher from China University of Geosciences Provides Details of New Studies and Findings in the Area of Machine Learning (Apatite trace element composition as an indicator of ore deposit types: A machine learning approach)
Researcher from China University of Geosciences Provides Details of New Studies and Findings in the Area of Machine Learning (Apatite trace element composition as an indicator of ore deposit types: A machine learning approach)
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NETL
NSTL
Mineralogical Soc Amer
Fresh data on artificial intelligence are presented in a new report. According to news originating from Beijing, People’s Republic of China, by NewsRx editors, the research stated, “The diverse suite of trace elements incorporated into apatite in ore-forming systems has important applications in petrogenesis studies of mineral deposits.” Our news editors obtained a quote from the research from China University of Geosciences: “Trace element variations in apatite can be used to distinguish between fertile and barren environments, and thus have potential as mineral exploration tools. Such classification approaches commonly employ two-variable scatterplots of apatite trace element compositional data. While such diagrams offer accessible visualization of compositional trends, they often struggle to effectively distinguish ore deposit types because they do not employ all the high-dimensional (i.e., multi-element) information accessible from high-quality apatite trace element analysis. To address this issue, we use a supervised machine-learning-based approach (eXtreme Gradient Boosting, XGBoost) to correlate apatite compositions with ore deposit type, utilizing such high-dimensional information. We evaluated 8629 apatite trace element data from five ore deposit types (porphyry, skarn, orogenic Au, iron oxide copper gold, and iron oxide-apatite) along with unmineralized magmatic and metamorphic apatite to identify discriminating parameters for the individual deposit types, as well as for mineralized systems. According to feature selection, eight elements (Th, U, Sr, Eu, Dy, Y, Nd, and La) improve the model performance.”
China University of GeosciencesBeijingPeople’s Republic of ChinaAsiaChemistryCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Mineralogical Soc Amer
