| A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications | |
He YY(何雨旸)1,2; Zhou, You3,4; Wen, Tao5; Zhang, Shuang6; Huang, Fang7 ; Zou, Xinyu8; Ma, Xiaogang9; Zhu, Yueqin10
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| Corresponding Author | He, Yuyang([email protected]) |
| Source Publication | APPLIED GEOCHEMISTRY
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| 2022-05-01 | |
| Volume | 140Pages:13 |
| ISSN | 0883-2927 |
| Abstract | The development of analytical and computational techniques and growing scientific funds collectively contribute to the rapid accumulation of geoscience data. The massive amount of existing data, the increasing complexity, and the rapid acquisition rates require novel approaches to efficiently discover scientific stories embedded in the data related to geochemistry and cosmochemistry. Machine learning methods can discover and describe the hidden patterns in intricate geochemical and cosmochemical big data. In recent years, considerable efforts have been devoted to the applications of machine learning methods in geochemistry and cosmochemistry. Here, we review the main applications including rock and sediment identification, digital mapping, water and soil quality prediction, and deep space exploration. Research method improvements, such as spectroscopy interpretation, numerical modeling, and molecular machine learning, are also discussed. Based on the up-to-date machine learning/deep learning techniques, we foresee the vast opportunities of implementing artificial intelligence and developing databases in geochemistry and cosmochemistry studies, as well as communicating geochemists/ cosmochemists and data scientists. |
| Keyword | LIBS XAFS Mapping Water soil prediction Molecular machine learning Reactive-transport modeling |
| DOI | 10.1016/j.apgeochem.2022.105273 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000799841900004 |
| WOS Keyword | INDUCED BREAKDOWN SPECTROSCOPY ; REACTIVE TRANSPORT MODELS ; UNDISCOVERED MINERAL-DEPOSITS ; ARTIFICIAL NEURAL-NETWORKS ; SUPPORT VECTOR MACHINE ; RANDOM FOREST ; CENTRAL VALLEY ; WATER-QUALITY ; THEORETICAL CALCULATION ; ISOTOPE FRACTIONATIONS |
| WOS Research Area | Geochemistry & Geophysics |
| WOS Subject | Geochemistry & Geophysics |
| Funding Project | National Science Foundation of China (NSFC)[42150202] ; National Science Foundation of China (NSFC)[4217030170] ; China Postdoctoral Science Foundation[2019M660811] ; pre-research project on Civil Aerospace Technologies of China National Space Administration[D020203] ; NSFC[41973063] ; NSFC[42011530431] ; Earth Science Information Partners Lab Grant[05088] ; NSFC project[42003021] ; NSFC project[2126315] ; U.S. National Science Foundation (NSF)[41872253] |
| Funding Organization | National Science Foundation of China (NSFC) ; China Postdoctoral Science Foundation ; pre-research project on Civil Aerospace Technologies of China National Space Administration ; NSFC ; Earth Science Information Partners Lab Grant ; NSFC project ; U.S. National Science Foundation (NSF) |
| Classification | 二类 |
| Ranking | 1 |
| Contributor | He, Yuyang |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/89574 |
| Collection | 高温气体动力学国家重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Earth & Planetary Phys, Beijing 100029, Peoples R China; 2.Chinese Acad Sci, Inst Mech, State Key Lab High Temp Gas Dynam, Beijing 100190, Peoples R China; 3.Chengdu Univ Technol, Coll Earth Sci, Int Res Ctr Planetary Sci, 61005, Chengdu, Peoples R China; 4.CAS Ctr Excellence Comparat Planetol, Hefei 230026, Peoples R China; 5.Syracuse Univ, Dept Earth & Environm Sci, Syracuse, NY 13244 USA; 6.Texas A&M Univ, Dept Oceanog, College Stn, TX 77843 USA; 7.CSIRO Mineral Resources, Kensington, WA 6151, Australia; 8.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Mineral Resources, Beijing 100029, Peoples R China; 9.Univ Idaho, Comp Sci Dept, 875 Perimeter Dr, MS 1010, Moscow, ID 83844 USA; 10.Minist Emergency Management Peoples Republ China, Natl Inst Nat Hazards, Beijing 100085, Peoples R China |
| Recommended Citation GB/T 7714 | He YY,Zhou, You,Wen, Tao,et al. A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications[J]. APPLIED GEOCHEMISTRY,2022,140:13.Rp_Au:He, Yuyang |
| APA | 何雨旸.,Zhou, You.,Wen, Tao.,Zhang, Shuang.,Huang, Fang.,...&Zhu, Yueqin.(2022).A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications.APPLIED GEOCHEMISTRY,140,13. |
| MLA | 何雨旸,et al."A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications".APPLIED GEOCHEMISTRY 140(2022):13. |
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| Jp2022FA388_2022_A r（1705KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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