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Basalt Tectonic Discrimination Using Combined Machine Learning Approach

文献类型：期刊论文

作者Ren, Qiubing1; Li, Mingchao1; Han, Shuai1; Zhang, Ye1; Zhang, Qi2; Shi, Jonathan3
刊名MINERALS
出版日期2019-06-01
卷号9期号:6页码:19
关键词basalt tectonic setting geochemical discrimination machine learning neural fuzzy inference system particle swarm optimization
ISSN号2075-163X
DOI10.3390/min9060376
英文摘要Geochemical discrimination of basaltic magmatism from different tectonic settings remains an essential part of recognizing the magma generation process within the Earth's mantle. Discriminating among mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB) is that matters to geologists because they are the three most concerned basalts. Being a supplement to conventional discrimination diagrams, we attempt to utilize the machine learning algorithm (MLA) for basalt tectonic discrimination. A combined MLA termed swarm optimized neural fuzzy inference system (SONFIS) was presented based on neural fuzzy inference system and particle swarm optimization. Two geochemical datasets of basalts from GEOROC and PetDB served as to test the classification performance of SONFIS. Several typical discrimination diagrams and well-established MLAs were also used for performance comparisons with SONFIS. Results indicated that the classification accuracy of SONFIS for MORB, OIB and IAB in both datasets could reach over 90%, superior to other methods. It also turns out that MLAs had certain advantages in making full use of geochemical characteristics and dealing with datasets containing missing data. Therefore, MLAs provide new research tools other than discrimination diagrams for geologists, and the MLA-based technique is worth extending to tectonic discrimination of other volcanic rocks.
WOS关键词PARTICLE SWARM OPTIMIZATION ; COMPRESSIVE STRENGTH ; BIG DATA ; N-MORB ; CLASSIFICATION ; PREDICTION ; DIAGRAMS ; ORIGIN ; ANFIS ; TI
资助项目National Natural Science Foundation for Excellent Young Scientists of China[51622904] ; Tianjin Science Foundation for Distinguished Young Scientists of China[17JCJQJC44000] ; National Natural Science Foundation for Innovative Research Groups of China[51621092]
WOS研究方向Mineralogy ; Mining & Mineral Processing
语种英语
WOS记录号WOS:000473809300049
出版者MDPI
资助机构National Natural Science Foundation for Excellent Young Scientists of China ; National Natural Science Foundation for Excellent Young Scientists of China ; Tianjin Science Foundation for Distinguished Young Scientists of China ; Tianjin Science Foundation for Distinguished Young Scientists of China ; National Natural Science Foundation for Innovative Research Groups of China ; National Natural Science Foundation for Innovative Research Groups of China ; National Natural Science Foundation for Excellent Young Scientists of China ; National Natural Science Foundation for Excellent Young Scientists of China ; Tianjin Science Foundation for Distinguished Young Scientists of China ; Tianjin Science Foundation for Distinguished Young Scientists of China ; National Natural Science Foundation for Innovative Research Groups of China ; National Natural Science Foundation for Innovative Research Groups of China ; National Natural Science Foundation for Excellent Young Scientists of China ; National Natural Science Foundation for Excellent Young Scientists of China ; Tianjin Science Foundation for Distinguished Young Scientists of China ; Tianjin Science Foundation for Distinguished Young Scientists of China ; National Natural Science Foundation for Innovative Research Groups of China ; National Natural Science Foundation for Innovative Research Groups of China ; National Natural Science Foundation for Excellent Young Scientists of China ; National Natural Science Foundation for Excellent Young Scientists of China ; Tianjin Science Foundation for Distinguished Young Scientists of China ; Tianjin Science Foundation for Distinguished Young Scientists of China ; National Natural Science Foundation for Innovative Research Groups of China ; National Natural Science Foundation for Innovative Research Groups of China
源URL[http://ir.iggcas.ac.cn/handle/132A11/92684]  
专题中国科学院地质与地球物理研究所
通讯作者Li, Mingchao
作者单位1.Tianjin Univ, State Key Lab Hydraul Engn Simulat & Safety, Tianjin 300354, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China
3.Louisiana State Univ, Coll Engn, Baton Rouge, LA 70803 USA
推荐引用方式
GB/T 7714		 Ren, Qiubing,Li, Mingchao,Han, Shuai,et al. Basalt Tectonic Discrimination Using Combined Machine Learning Approach[J]. MINERALS,2019,9(6):19.
APA Ren, Qiubing,Li, Mingchao,Han, Shuai,Zhang, Ye,Zhang, Qi,&Shi, Jonathan.(2019).Basalt Tectonic Discrimination Using Combined Machine Learning Approach.MINERALS,9(6),19.
MLA Ren, Qiubing,et al."Basalt Tectonic Discrimination Using Combined Machine Learning Approach".MINERALS 9.6(2019):19.

入库方式： OAI收割

来源：地质与地球物理研究所

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