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Basalt Tectonic Discrimination Using Combined Machine Learning Approach
Ren, Qiubing1; Li, Mingchao1; Han, Shuai1; Zhang, Ye1; Zhang, Qi2; Shi, Jonathan3
2019-06-01
Source PublicationMINERALS
ISSN2075-163X
Volume9Issue:6Pages:19
AbstractGeochemical 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.
Keywordbasalt tectonic setting geochemical discrimination machine learning neural fuzzy inference system particle swarm optimization
DOI10.3390/min9060376
Funding OrganizationNational 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
WOS KeywordPARTICLE SWARM OPTIMIZATION ; COMPRESSIVE STRENGTH ; BIG DATA ; N-MORB ; CLASSIFICATION ; PREDICTION ; DIAGRAMS ; ORIGIN ; ANFIS ; TI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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
WOS Research AreaMineralogy ; Mining & Mineral Processing
WOS SubjectMineralogy ; Mining & Mineral Processing
WOS IDWOS:000473809300049
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/92684
Collection中国科学院地质与地球物理研究所
Corresponding AuthorLi, Mingchao
Affiliation1.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
Recommended Citation
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.
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