首页|Researcher from China University of Geosciences Publishes Findings in Machine Le arning (A machine learning approach to discrimination of igneous rocks and ore d eposits by zircon trace elements)
Researcher from China University of Geosciences Publishes Findings in Machine Le arning (A machine learning approach to discrimination of igneous rocks and ore d eposits by zircon trace elements)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting out of Beijing, Pe ople's Republic of China, by NewsRx editors, research stated, "The mineral zirco n has a robust crystal structure, preserving a wealth of geological information through deep time. Traditionally, trace elements in magmatic and hydrothermal zi rcon have been employed to distinguish between different primary igneous or meta llogenic growth fluids." The news reporters obtained a quote from the research from China University of G eosciences: "However, classical approaches based on mineral geochemistry are not only time consuming but often ambiguous due to apparent compositional overlap f or different growth environments. Here, we report a compilation of 11 004 zircon trace element measurements from 280 published articles, 7173 from crystals in i gneous rocks, and 3831 from ore deposits. Geochemical variables include Hf, Th, U, Y, Ti, Nb, Ta, and the REEs. Igneous rock types include kimberlite, carbonati te, gabbro, basalt, andesite, diorite, granodiorite, dacite, granite, rhyolite, and pegmatite. Ore types include porphyry Cu-Au-Mo, skarn-type polymetallic, int rusion-related Au, skarn-type Fe-Cu, and Nb-Ta deposits. We develop Decision Tre e, XGBoost, and Random Forest algorithms with this zircon geochemical informatio n to predict lithology or deposit type. The F1-score indicates that the Random F orest algorithm has the best predictive performance for the classification of bo th lithology and deposit type."
China University of GeosciencesBeijingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Lear ning
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