A comparison of tree-based ensemble algorithms on the main element content of monoclinal pyroxene in mafic-ultramafic rocks

SUN Jiankun; DU Xueliang; ZHANG Baoyue; WANG Long; JIN Weijun; ZHANG Qi; LUO Xiong; ZHU Yueqin

2019 Vol. 38, No. 12

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Article navigation > Geological Bulletin of China > 2019 > 38(12): 1981-1991

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SUN Jiankun, DU Xueliang, ZHANG Baoyue, WANG Long, JIN Weijun, ZHANG Qi, LUO Xiong, ZHU Yueqin. A comparison of tree-based ensemble algorithms on the main element content of monoclinal pyroxene in mafic-ultramafic rocks[J]. Geological Bulletin of China, 2019, 38(12): 1981-1991.

Citation:

SUN Jiankun, DU Xueliang, ZHANG Baoyue, WANG Long, JIN Weijun, ZHANG Qi, LUO Xiong, ZHU Yueqin. A comparison of tree-based ensemble algorithms on the main element content of monoclinal pyroxene in mafic-ultramafic rocks[J]. Geological Bulletin of China, 2019, 38(12): 1981-1991.

A comparison of tree-based ensemble algorithms on the main element content of monoclinal pyroxene in mafic-ultramafic rocks

1.
School of Computer and Communication Engineering, University of Science and Technology Beijing(USTB), Beijing 100083, China
2.
Key Laboratory of Geological Information Technology, MNR, Beijing 100037, China
3.
Key Laboratory of Mineral Resources in Western China(Gansu Province)/School of Earth Sciences, Lanzhou University, Lanzhou 730000, Gansu, China
4.
Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China

More Information

Corresponding author: LUO Xiong, xluo@ustb.edu.cn

Received Date: 16 April 2019

Revised Date: 23 July 2019

Publish Date: 15 December 2019

Abstract

Abstract

Relying on the geochemical composition of the magma tectonic environment to understand the formation process of magma is an important application in rock geochemistry. While the current works to make full use of rock geochemical components for the tectonic setting discrimination are not enough. In this study, the authors utilized four tree-based machine learning methods to make magma tectonic environment discriminations and feature sorting on the 13 main ingredients of monoclinal pyroxene in maficultramafic rocks from global Cenozoic ocean island (OIB), island arc (IAB), and mid-ocean ridge (MORB). Through the comparison of the four tree-based machine learning methods, the authors proved the validity of the tree-based methods for the identification of geochemical components and derived the advantages and disadvantages of the four methods in dealing with the identification of rock tectonic environments:decision trees gain better comprehensibility but have lower recognition accuracy, boosting algorithms AdaBoost and GBDT have the best recognition accuracy but lower comprehensibility, and random forest is a better choice during trading off and comprehensibility performance. Besides, Cr₂O₃, TFeO, TiO₂, FeO and Al₂O₃ are figured out as the most important ingredients for magma tectonic environment discriminations on this dataset.
- tree algorithm /
- bagging algorithm /
- boosting algorithm /
- magmatic environment discrimination /
- clinopyroxene /
- geochemical characteristics

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References

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