Earth Science Frontiers ›› 2022, Vol. 29 ›› Issue (5): 464-475.DOI: 10.13745/j.esf.sf.2022.2.75

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Machine learning-based approach for zircon classification and genesis determination

ZHU Ziyi1(), ZHOU Fei1, WANG Yu1, ZHOU Tong1, HOU Zhaoliang2, QIU Kunfeng1,3,*()   

  1. 1. School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
    2. Department of Geology, University of Vienna, Vienna 1090, Austria
    3. State Key Laboratory of Geological Process and Mineral Resources, China University of Geosciences (Beijing), Beijing 100083, China
  • Received:2021-12-16 Revised:2022-03-18 Online:2022-09-25 Published:2022-08-24
  • Contact: QIU Kunfeng


Zircon, a stable paragenetic mineral in various geological environments, has been recognized as a great tool in the chronological study of primary rocks. Trace elements in zircons may reveal geological evolutionary processes, and have long been used in zircon classification and zircon formation studies by binary diagram method, such as Th-U and LaN-(Sm/La)N diagrams. However, with the massive increase of zircon research, the traditional binary diagrams are no longer adequate for a precise determination of zircon types because binary plots cannot display higher dimensional information and therefore can lead to erroneous interpretation of zircon data. To address this issue, we take a machine learning approach to analyzing 3 498 zircon trace element data for different zircon genetic types to obtain high-dimensional zircon classification diagrams. We tested four machine learning algorithms (random forest, support vector machine, artificial neural network, and k-nearest neighbor) and consider support vector machine, with an 86.8% accuracy in predicting zircon type and origin, can best contribute to zircon classification. In addition to the development of a high-dimensional zircon classification diagram, this work also greatly improves the accuracy of zircon genesis determination using trace elements, and demonstrates the applicability of modern data science technique in geochemical research.

Key words: zircon, trace elements, formation, big data analysis, machine learning

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