A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications

1,2He, Yuyang3,4Zhou, You,5Wen, Tao,6Zhang, Shuang,7Huang, Fang,8Zou, Xinyu,9Ma, Xiaogang,10Zhu, Yueqin
Applied Geochemistry 140, 105273 Link to Article [DOI 10.1016/j.apgeochem.2022.105273]
1Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, 100029, China
2State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, Beijing, 100190, China
3International Research Center for Planetary Science, College of Earth Sciences, Chengdu University of Technology, Chengdu, 61005, China
4CAS Center for Excellence in Comparative Planetology, Hefei, 230026, China
5Department of Earth and Environmental Sciences, Syracuse University, Syracuse, 13244, NY, United States
6Department of Oceanography, Texas A&M University, College Station, 77843, TX, United States
7CSIRO Mineral Resources, Kensington, 6151, WA, Australia
8Key Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, 100029, China
9Computer Science Department, University of Idaho, 875 Perimeter Drive, MS 1010, Moscow, 83844-1010, ID, United States
10National Institute of Natural Hazards, Ministry of Emergency Management of the People’s Republic of China, Beijing, 100085, China

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