Tectonic discrimination of oceanic basalt by machine learning

XU Kun; GUAN Xiner; LV Haozhe; ZHAO Xiao; Rezeye RUZI; CHEN Yanhong

doi:10.16562/j.cnki.0256-1492.2023041101

Marine Geology & Quaternary Geology > 2024 > 44(4): 190-199. > DOI: 10.16562/j.cnki.0256-1492.2023041101

XU Kun，GUAN Xiner，LV Haozhe，et al. Tectonic discrimination of oceanic basalt by machine learning[J]. Marine Geology & Quaternary Geology，2024，44(4)：190-199. DOI: 10.16562/j.cnki.0256-1492.2023041101

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Tectonic discrimination of oceanic basalt by machine learning

1.
School of Ocean Sciences, China University of Geosciences, Beijing 100083, China
2.
Key Laboratory of Marine Mineral Resources and Polar Geology, Ministry of Education, China University of Geosciences, Beijing 100083, China

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Received Date: April 10, 2023
Revised Date: May 31, 2023
Available Online: May 27, 2024

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Abstract

Abstract

The geochemical composition of basalt is closely related to the tectonic setting of the formation, thus basalt is an important window for viewing the deep Earth and the composition and geodynamic processes. To discriminate the tectonic setting of basalt formation, although a series of tectonic discrimination diagrams have been established based on the geochemical characteristics of basalt, those discrimination diagrams are limited to two-dimensional or three-dimensional data. With the explosive growth of global geochemical data of basalt, these discrimination diagrams show gradually the shortcomings of being local and inaccurate. Therefore, using machine learning methods is beneficial to analyze data multi-dimensionally and comprehensively, and to establish accurate and efficient discriminant models. A global modern oceanic basalt dataset was established by using GEOROC and PetDB databases through a series of steps from data downloading, training, and analyzing. The dataset was trained by the support vector machine (SVM) and random forest (RF) machine learning algorithms and a high-accuracy and high-dimensional discrimination model was built. In addition, the accuracies of different machine-learning algorithms training were analyzed against different geochemical composition datasets of modern oceanic basalt, and the discrimination models were applied to ophiolitic basalt to explore the application of machine learning models for ancient oceanic basalt. This work provided a higher-dimensional approach to discriminate oceanic basalt, and a successful attempt of using machine learning in earth science in the era of the big data.
- geochemistry,
- tectonic setting,
- machine learning,
- oceanic basalt

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References (31)

References

[1]	White W M. Probing the Earth’s deep interior through geochemistry[J]. Geochemical Perspectives, 2015, 4(2):95-96.
[2]	Doucet L S, Tetley M G, Li Z X, et al. Geochemical fingerprinting of continental and oceanic basalts: A machine learning approach[J]. Earth-Science Reviews, 2022, 233:104192. doi: 10.1016/j.earscirev.2022.104192
[3]	Pearce J A. Role of the sub-continental lithosphere in magma genesis at active continental margins[M]//Hawkesworth C J, Norry M J. Continental Basalts and Mantle Xenoliths. Nantwich, Cheshire: Shiva Publications, 1983: 230-249.
[4]	Pearce J A. Geochemical fingerprinting of oceanic basalts with applications to ophiolite classification and the search for Archean oceanic crust[J]. Lithos, 2008, 100(1-4):14-48. doi: 10.1016/j.lithos.2007.06.016
[5]	Wood D A. The application of a Th-Hf-Ta diagram to problems of tectonomagmatic classification and to establishing the nature of crustal contamination of basaltic lavas of the British Tertiary Volcanic Province[J]. Earth and Planetary Science Letters, 1980, 50(1):11-30. doi: 10.1016/0012-821X(80)90116-8
[6]	Shervais J W. Ti-V plots and the petrogenesis of modern and ophiolitic lavas[J]. Earth and Planetary Science Letters, 1982, 59(1):101-118. doi: 10.1016/0012-821X(82)90120-0
[7]	Pearce J A. Trace element characteristics of lavas from destructive plate boundaries[M]//Thorpe R S. Orogenic Andesites and Related Rocks. Chichester, England: John Wiley and Sons, 1982: 528-548.
[8]	Rollinson H, Pease V. Using Geochemical Data: To Understand Geological Processes[M]. 2nd ed. Cambridge: Cambridge University Press, 2021: 226-278.
[9]	第鹏飞, 王金荣, 张旗, 等. 玄武岩构造环境判别图评估—全体数据研究的启示[J]. 矿物岩石地球化学通报, 2017, 36(6):891-896,879 DI Pengfei, WANG Jinrong, ZHANG Qi, et al. The evaluation of basalt tectonic discrimination diagrams: Constraints on the research of global basalt data[J]. Bulletin of Mineralogy, Petrology and Geochemistry, 2017, 36(6):891-896,879.]
[10]	Vermeesch P. Tectonic discrimination of basalts with classification trees[J]. Geochimica et Cosmochimica Acta, 2006, 70(7):1839-1848. doi: 10.1016/j.gca.2005.12.016
[11]	周永章, 王俊, 左仁广, 等. 地质领域机器学习、深度学习及实现语言[J]. 岩石学报, 2018, 34(11):3173-3178 ZHOU Yongzhang, WANG Jun, ZUO Renguang, et al. Machine learning, deep learning and Python language in field of geology[J]. Acta Petrologica Sinica, 2018, 34(11):3173-3178.]
[12]	Bergen K J, Johnson P A, De Hoop M V, et al. Machine learning for data-driven discovery in solid Earth geoscience[J]. Science, 2019, 363(6433):eaau0323. doi: 10.1126/science.aau0323
[13]	周志华. 机器学习[M]. 北京: 清华大学出版社, 2016 ZHOU Zhihua. Machine Learning[M]. Beijing: Tsinghua University Press, 2016.]
[14]	刘坤, 刘文波. 机器学习与大陆板内玄武岩构造环境判别[J]. 工程技术与管理, 2017, 1(2):188-191 LIU Kun, LIU Wenbo. Machine learning and identification of the tectonic environment of basalt in the continental plate[J]. Engineering Technology & Management, 2017, 1(2):188-191.]
[15]	焦守涛, 周永章, 张旗, 等. 基于GEOROC数据库的全球辉长岩大数据的大地构造环境智能判别研究[J]. 岩石学报, 2018, 34(11):3189-3194 JIAO Shoutao, ZHOU Yongzhang, ZHANG Qi, et al. Study on intelligent discrimination of tectonic settings based on global gabbro data from GEOROC[J]. Acta Petrologica Sinica, 2018, 34(11):3189-3194.]
[16]	任秋兵, 李明超, 李玉琼, 等. 基于全球橄榄石数据的玄武岩构造环境智能判别方法及其验证[J]. 大地构造与成矿学, 2020, 44(2):212-221 REN Qiubing, LI Mingchao, LI Yuqiong, et al. An intelligent method for geochemical discrimination of tectonic settings of basalt based on olivine composition: GWO-SVM method and its verification[J]. Geotectonica et Metallogenia, 2020, 44(2):212-221.]
[17]	Guo P, Yang T, Xu W L, et al. Machine learning reveals source compositions of intraplate basaltic rocks[J]. Geochemistry, Geophysics, Geosystems, 2021, 22(9):e2021GC009946. doi: 10.1029/2021GC009946
[18]	余星. 海底岩石地球化学研究中的"大数据": PetDB及其应用[J]. 地球科学进展, 2014, 29(2):306-314 YU Xing. The big data tool for seabed petrogeochemistry research-PetDB and its application in geoscience[J]. Advances in Earth Science, 2014, 29(2):306-314.]
[19]	葛粲, 汪方跃, 李永东, 等. 基于GEOROC大数据分析地壳厚度地球化学指标[J]. 岩石学报, 2018, 34(11):3179-3188 GE Can, WANG Fangyue, LI Yongdong, et al. Analysis of geochemical indices of crustal thickness based on GEOROC big data[J]. Acta Petrologica Sinica, 2018, 34(11):3179-3188.]
[20]	张晓琴, 程誉莹. 基于随机森林模型的成分数据缺失值填补法[J]. 应用概率统计, 2017, 33(1):102-110 ZHANG Xiaoqin, CHENG Yuying. Imputation of missing values for compositional data based on random forest[J]. Chinese Journal of Applied Probability and Statistics, 2017, 33(1):102-110.]
[21]	朱紫怡, 周飞, 王瑀, 等. 基于机器学习的锆石成因分类研究[J]. 地学前缘, 2022, 29(5):464-475 ZHU Ziyi, ZHOU Fei, WANG Yu, et al. Machine learning-based approach for zircon classification and genesis determination[J]. Earth Science Frontiers, 2022, 29(5):464-475.]
[22]	Breiman L. Using iterated bagging to debias regressions[J]. Machine Learning, 2001, 45(3):261-277. doi: 10.1023/A:1017934522171
[23]	Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273-297.
[24]	Pearce J A. Immobile element fingerprinting of ophiolites[J]. Elements, 2014, 10(2):101-108. doi: 10.2113/gselements.10.2.101
[25]	Dai J G, Wang C S, Stern R J, et al. Forearc magmatic evolution during subduction initiation: Insights from an Early Cretaceous Tibetan ophiolite and comparison with the Izu-Bonin-Mariana forearc[J]. GSA Bulletin, 2021, 133(3-4):753-776. doi: 10.1130/B35644.1
[26]	Clarke D B, Cameron B I, Muecke G K, et al. Early Tertiary basalts from the Labrador Sea floor and Davis Strait region[J]. Canadian Journal of Earth Sciences, 1989, 26(5):956-968. doi: 10.1139/e89-077
[27]	Deng H, Peng S B, Polat A, et al. Neoproterozoic IAT intrusion into Mesoproterozoic MOR Miaowan Ophiolite, Yangtze Craton: evidence for evolving tectonic settings[J]. Precambrian Research, 2017, 289:75-94. doi: 10.1016/j.precamres.2016.12.003
[28]	Güneş A, İlbeyli N, Rasimgil S, et al. Petrological and geochemical characteristics of the diabase and metasomatised dikes from the Tekirova ophiolite (SW Anatolia, Turkey): Tectonomagmatic evolution of the southern Neotethys[J]. Geochemistry, 2021, 81(3):125767. doi: 10.1016/j.chemer.2021.125767
[29]	熊庆. 蛇绿岩记录的大洋地幔内熔体迁移过程[J]. 矿物岩石地球化学通报, 2021, 40(5):999-1011 doi: 10.19658/j.issn.1007-2802.2021.40.043 XIONG Qing. Ophiolitic records of melt migration processes in oceanic mantle[J]. Bulletin of Mineralogy, Petrology and Geochemistry, 2021, 40(5):999-1011.] doi: 10.19658/j.issn.1007-2802.2021.40.043
[30]	卢泓宇, 张敏, 刘奕群, 等. 卷积神经网络特征重要性分析及增强特征选择模型[J]. 软件学报, 2017, 28(11):2879-2890 doi: 10.13328/j.cnki.jos.005349 LU Hongyu, ZHANG Min, LIU Yiqun, et al. Convolution neural network feature importance analysis and feature selection enhanced model[J]. Journal of Software, 2017, 28(11):2879-2890.] doi: 10.13328/j.cnki.jos.005349
[31]	赵庆媛, 叶春茂, 鲁耀兵. 基于随机森林的微动特征重要性评估研究[J]. 现代防御技术, 2022, 50(4):124-131 ZHAO Qingyuan, YE Chunmao, LU Yaobing. A micro-motion feature importance evaluation algorithm based on random forest[J]. Modern Defence Technology, 2022, 50(4):124-131.]

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Tectonic discrimination of oceanic basalt by machine learning