基于机器学习的埃达克质岩构造背景判别研究

doi:10.13745/j.esf.sf.2023.9.2

地学前缘 ›› 2024, Vol. 31 ›› Issue (4): 417-428.DOI: 10.13745/j.esf.sf.2023.9.2

• 非主题来稿选登：人工智能与地质应用 • 上一篇下一篇

基于机器学习的埃达克质岩构造背景判别研究

张焕宝¹(), 贺海洋¹^,^*(), 杨仕教¹, 李亚林², 毕文军³, 韩世礼¹, 郭钦鹏¹, 杜青¹

1.南华大学资源环境与安全工程学院, 湖南衡阳 421001
2.中国地质大学(北京) 地球科学与资源学院, 北京 100083
3.太原理工大学矿业工程学院, 山西太原 030024

收稿日期:2023-05-26 修回日期:2023-07-27 出版日期:2024-07-25 发布日期:2024-07-10
通信作者: * 贺海洋(1991—),男,博士,讲师,硕士生导师,主要从事机器学习及大地构造学研究工作。E-mail: hehy@usc.edu.cn
作者简介:张焕宝(1991—),女,博士研究生,主要从事人工智能及大地构造学研究工作。E-mail: 2019000068@usc.edu.cn
基金资助:
湖南省自然科学基金面上项目(2023JJ30507);湖南省自然科学基金面上项目(2023JJ30506);山西省自然科学基金青年项目(202103021223120);湖南省教育厅科学研究项目(22B0433)

Machine learning-based approach for adakitic rocks tectonic setting determination

ZHANG Huanbao¹(), HE Haiyang¹^,^*(), YANG Shijiao¹, LI Yalin², BI Wenjun³, HAN Shili¹, GUO Qinpeng¹, DU Qing¹

1. School of Resources Environment and Safety Engineering, University of South China, Hengyang 421001, China
2. School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
3. College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Received:2023-05-26 Revised:2023-07-27 Online:2024-07-25 Published:2024-07-10

摘要/Abstract

摘要：

埃达克质岩具有重要的地球动力学和金属成矿意义,其构造背景的准确识别为探讨区域构造-岩浆演化过程提供了重要依据。由于埃达克质岩源区、热源和岩浆产生机制的多样性,传统低维度地球化学手段在识别构造背景时存在局限性。随着地学数据的指数增长和人工智能的发展,机器学习为解决该问题提供了新方法。因此,本文将机器学习与地质大数据相结合,构建高精度埃达克质岩构造背景判别模型和可视化图解。文中收集了1 075条全球埃达克质岩主、微量地球化学数据,使用主成分分析和t分布-随机近邻嵌入等无监督学习方法进行高维数据降维,采用随机森林、支持向量机、人工神经网络和K近邻等机器学习方法进行数据训练,得出准确率为98.5%的高斯核支持向量机埃达克质岩构造背景判别器,并提出Ba-Sr/Nd图解,为汇聚板块边缘、板内火山活动和太古宙克拉通(包括绿岩带)3种构造背景判别提供依据。这项工作将拓展机器学习在埃达克质岩构造背景研究中的应用,为构造-岩浆作用研究带来新的思路。

关键词: 埃达克质岩, 构造背景, 判别图解, 主、微量元素, 大数据分析, 机器学习

Abstract:

Adakitic rocks hold significant geodynamic and metallogenic implications, and accurately determining their tectonic setting is crucial for understanding regional tectonic-magmatic evolution. However, due to the diverse sources, heat regimes, and magma generation mechanisms of adakitic rocks, conventional low-dimensional geochemical methods face limitations in tectonic setting identification. With the exponential growth of geoscience data and advancements in artificial intelligence, machine learning offers a novel approach to address this challenge. In this study, we integrate machine learning with geological big data to develop a high-precision adakitic tectonic setting discrimination model and visual representation. We compiled major and trace elements geochemical data from 1075 adakitic rocks worldwide and employed unsupervised learning techniques such as principal component analysis and t-distributed stochastic neighbor embedding for high-dimensional data reduction. Various machine learning algorithms including random forest, support vector machine, artificial neural network, and K-nearest neighbor were trained. Consequently, we established a Gaussian kernel support vector machine adakitic rock tectonic setting discriminator with 98.5% accuracy and proposed a Ba versus Sr/Nd diagram to delineate three tectonic settings: convergent margin, intraplate volcanism, and Archean craton (comprising greenstone belts). This study broadens the application of machine learning in adakitic rock tectonic setting analysis, offering fresh insights into tectonic-magmatic processes investigation.

Key words: adakitic rock, tectonic setting, discrimination model, major and trace elements, big data analysis, machine learning

中图分类号:

张焕宝, 贺海洋, 杨仕教, 李亚林, 毕文军, 韩世礼, 郭钦鹏, 杜青. 基于机器学习的埃达克质岩构造背景判别研究[J]. 地学前缘, 2024, 31(4): 417-428.

ZHANG Huanbao, HE Haiyang, YANG Shijiao, LI Yalin, BI Wenjun, HAN Shili, GUO Qinpeng, DU Qing. Machine learning-based approach for adakitic rocks tectonic setting determination[J]. Earth Science Frontiers, 2024, 31(4): 417-428.

图/表 12

表1 埃达克质岩不同构造背景类型主、微量元素数据量

Table 1 Quantity of major and trace element data of adakitic rocks from different sources

序号	构造背景类型	数据量/条
1	汇聚板块边缘	583
2	板内火山活动	315
3	太古宙克拉通(包括绿岩带)	177

图1 埃达克质岩数据在全球分布情况

Fig.1 Global distribution of the collected adakitic rock data

表2 不同构造背景类型埃达克质岩主微量元素数据量缺失情况

Table 2 Missing data of adakitic rock major and trace elements from different provenance

图2 埃达克质岩主、微量元素数据含量箱形图

Fig.2 Box plot of major and trace elements concentration in adakitic rocks

图3 混淆矩阵

Fig.3 Confusion matrix

图4 (a) 混淆矩阵基于高斯核支持向量机分类器的验证结果;(b) 高斯核支持向量机在不同埃达克质岩构造背景分类的学习曲线

Fig.4 (a) Confusion matrix based on validation results of Gaussian SVM classifier; (b) Learning curve of Gaussian SVM classifier in classifying different tectonic setting types of adakitic rock

图5 4种机器学习分类算法在不同埃达克质岩构造背景判别的决策边界问题上的比较

Fig.5 Comparison of four machine learning classification algorithms on the decision problem of distinguishing different tectonic setting types of adakitic rocks

图6 PCA及t-SNE分析结果:(a)PCA载荷图;(b)t-SNE图

Fig.6 PCA and t-SNE analysis results. (a) PCA loading plot; (b) t-SNE plot.

图7 混淆矩阵基于4种分类器的验证结果

Fig.7 Confusion matrix based on validation results of four classifiers

图8 重要特征元素组合PCA载荷图

Fig.8 Loading plot of important feature element combinations in PCA

表3 重要特征元素组合PCA成分矩阵权重

Table 3 The PCA component matrix of important characteristic elements

主成分	特征元素组合的权重
主成分	Sr/Nd	Ba	Ce	SiO₂/La	Al₂O₃/Ba	Sr/Hf	Rb/Ni	Sr/Pr	Sr/Zr	Sr/La	TiO₂/CaO	Pr
PC1 (46.0%)	0.398 07	0.025 32	-0.293 69	0.277 49	0.001 62	0.310 43	-0.205 32	0.408 68	0.291 53	0.398 02	-0.219 01	-0.283 56
PC2 (31.2%)	0.132 42	0.460 18	0.343 38	-0.352 9	-0.451 24	0.234 32	-0.118 09	0.114 08	0.279 38	0.116 22	0.167 87	0.350 74

图9 埃达克质岩构造背景判别图解

Fig.9 Discriminating diagram for different tectonic setting types of adakitic rock

参考文献 72

[1]	DEFANT M J, DRUMMOND M S. Derivation of some modern arc magmas by melting of young subducted lithosphere[J]. Nature, 1990, 347(6294): 662-665.
[2]	HOU Z Q, ZHENG Y C, ZENG L S, et al. Eocene-Oligocene granitoids in southern Tibet: constraints on crustal anatexis and tectonic evolution of the Himalayan Orogen[J]. Earth and Planetary Science Letters, 2012, 349: 38-52.
[3]	LAI S C, QIN J F, LI Y F, et al. Cenozoic volcanic rocks in the Belog Co area, Qiangtang, northern Tibet, China: petrochemical evidence for partial melting of the mantle-crust transition zone[J]. Chinese Journal of Geochemistry, 2007, 26(3): 305-311.
[4]	XU J F, SHINJO R, DEFANT M J, et al. Origin of Mesozoic adakitic intrusive rocks in the Ningzhen area of East China: partial melting of delaminated lower continental crust?[J]. Geology, 2002, 30(12): 1111.
[5]	王强, 郝露露, 张修政, 等. 汇聚板块边缘的埃达克质岩: 成分和成因[J]. 中国科学: 地球科学, 2020, 50(12): 1845-1873.
[6]	CASTILLO P R. 埃达克岩成因回顾[J]. 科学通报, 2006, 51(6): 617-627.
[7]	LE MAITRE R W. Igneous rocks, a classification and glossary of terms[M]. New York: Cambridge University Press, 2002: 1-254.
[8]	孙立强, 凌洪飞, 赵葵东, 等. 华夏地块早白垩世埃达克质岩的岩石成因及地质意义[J]. 中国科学: 地球科学, 2017, 47(7): 783-803.
[9]	ZHANG L Y, LI S C, ZHAO Q Y. A review of research on adakites[J]. International Geology Review, 2021, 63(1): 47-64.
[10]	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.
[11]	CONDIE K C. Geochemistry and tectonic setting of early Proterozoic supracrustal rocks in the southwestern United States[J]. The Journal of Geology, 1986, 94(6): 845-864.
[12]	HARRIS N B W, PEARCE J A, TINDLE A G. Geochemical characteristics of collision-zone magmatism[M]. London: Geological Society, 1986: 69-81.
[13]	PEARCE J A, HARRIS N B W, TINDLE A G. Trace element discrimination diagrams for the tectonic interpretation of granitic rocks[J]. Journal of Petrology, 1984, 25(4): 956-983.
[14]	PEARCE J A. Geochemical fingerprinting of oceanic basalts with applications to ophiolite classification and the search for Archean oceanic crust[J]. Lithos, 2008, 100(1/2/3/4): 14-48.
[15]	WANG Q, XU J F, JIAN P, et al. Petrogenesis of adakitic porphyries in an extensional tectonic setting, Dexing, South China: implications for the genesis of porphyry copper mineralization[J]. Journal of Petrology, 2006, 47(1): 119-144.
[16]	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.
[17]	LI C S, ARNDT N T, TANG Q Y, et al. Trace element indiscrimination diagrams[J]. Lithos, 2015, 232: 76-83.
[18]	成秋明. 什么是数学地球科学及其前沿领域?[J]. 地学前缘, 2021, 28(3): 6-25. DOI
[19]	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.
[20]	周永章, 陈烁, 张旗, 等. 大数据与数学地球科学研究进展: 大数据与数学地球科学专题代序[J]. 岩石学报, 2018, 34(2): 255-263.
[21]	周永章, 张良均, 张奥多, 等. 地球科学大数据挖掘与机器学习[M]. 广州: 中山大学出版社, 2018.
[22]	谭永杰. 地质大数据体系建设的总体框架研究[J]. 中国地质调查, 2016, 3(3): 1-6.
[23]	周永章, 左仁广, 刘刚, 等. 数学地球科学跨越发展的十年: 大数据、人工智能算法正在改变地质学[J]. 矿物岩石地球化学通报, 2021, 40(3): 556-573, 777.
[24]	肖立志. 机器学习数据驱动与机理模型融合及可解释性问题[J]. 石油物探, 2022, 61(2): 205-212. DOI
[25]	吴冲龙, 刘刚, 张夏林, 等. 地质科学大数据及其利用的若干问题探讨[J]. 科学通报, 2016, 61(16): 1797-1807.
[26]	朱月琴, 谭永杰, 张建通, 等. 基于Hadoop的地质大数据融合与挖掘技术框架[J]. 测绘学报, 2015, 44(增刊1): 152-159.
[27]	ZHAO Y, ZHANG Y G, GENG M, et al. Involvement of slab-derived fluid in the generation of Cenozoic basalts in Northeast China inferred from machine learning[J]. Geophysical Research Letters, 2019, 46(10): 5234-5242.
[28]	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.
[29]	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.
[30]	陈伟雄, 杨华健, 周泽东, 等. 地质矿物的地球化学数据可视化分析与构造背景预测[J]. 世界有色金属, 2019(14): 132-134.
[31]	韩帅, 李明超, 任秋兵, 等. 基于大数据方法的玄武岩大地构造环境智能挖掘判别与分析[J]. 岩石学报, 2018, 34(11): 3207-3216.
[32]	任秋兵, 李明超, 韩帅. 基于改进遗传算法-神经网络的玄武岩构造环境判别及对比实验[J]. 地学前缘, 2019, 26(4): 117-124. DOI
[33]	杜雪亮, 李玉琼, 杜君, 等. 全球大陆板内玄武岩数据挖掘[J]. 矿物岩石地球化学通报, 2017, 36(6): 905-911, 879.
[34]	杨婧, 王金荣, 张旗, 等. 全球岛弧玄武岩数据挖掘: 在玄武岩判别图上的表现及初步解释[J]. 地质通报, 2016, 35(12): 1937-1949.
[35]	ZHONG R C, DENG Y, YU C. Multi-layer perceptron-based tectonic discrimination of basaltic rocks and an application on the Paleoproterozoic Xiong’er volcanic province in the North China Craton[J]. Computers and Geosciences, 2021, 149: 104717.
[36]	GELADI P, ISAKSSON H, LINDQVIST L, et al. Principal component analysis of multivariate images[J]. Chemometrics and Intelligent Laboratory Systems, 1989, 5(3): 209-220.
[37]	WOLD S, GELADI P, ESBENSEN K, et al. Multi-way principal components and PLS-analysis[J]. Journal of Chemometrics, 1987, 1(1): 41-56.
[38]	MAATEN L V D, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(2): 2579-2605.
[39]	BREIMAN L. Using iterated bagging to debias regressions[J]. Machine Learning, 2001, 45(3): 261-277.
[40]	HSU C W, CHANG C C, LIN C J. A practical guide to support vector classification[J]. BJU International, 2003, 101(1): 1396-1400.
[41]	KOHONEN T. An introduction to neural computing[J]. Neural Networks, 1988, 1(1): 3-16.
[42]	ZHANG M L, ZHOU Z H. ML-KNN: a lazy learning approach to multi-label learning[J]. Pattern Recognition, 2007, 40(7): 2038-2048.
[43]	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.
[44]	熊佳杰, 李嘉诚, 徐柳林, 等. 基于GEOROC和PetDB全球岩石地球化学数据库的洋内岛弧和陆缘弧构造背景判别研究[J]. 四川地质学报, 2022, 42(2): 321-324.
[45]	周皓. 吉南—辽东地区早白垩世火山岩成因: 来自地球化学和Sr-Nd-Pb同位素的制约[D]. 长春: 吉林大学, 2021.
[46]	章家保, 金翔龙, 高金耀, 等. 断裂和白垩纪岩浆活动对中西太平洋海山区海山形成的影响[J]. 海洋地质与第四纪地质, 2006, 26(1): 67-74.
[47]	ZOU S H, CHEN X L, XU D R, et al. A machine learning approach to tracking crustal thickness variations in the eastern North China Craton[J]. Geoscience Frontiers, 2021, 12(5): 101195.
[48]	PETRELLI M, CARICCHI L, PERUGINI D. Machine learning thermo-barometry: application to clinopyroxene-bearing magmas[J]. Journal of Geophysical Research: Solid Earth, 2020, 125(9): e2020JB020130.
[49]	DENG Y, ZHONG R C, LI D F, et al. Hunting the datable garnet using the LA-ICP-MS U-Pb method: predicting garnet U concentration, based on major and minor elements[J]. Acta Geologica Sinica (English Edition), 2022, 96(6): 2148-2157.
[50]	ROSSITER H E, BOUDRIAS M H, WARD N S. Do movement-related beta oscillations change after stroke?[J]. Journal of Neurophysiology, 2014, 112(9): 2053-2058. DOI PMID
[51]	ZHANG Z J, ZHOU Y Z, ZHANG P. Crucial geochemical signal identification for Cu-fertile magmas in paleo-Tethyan arc based on machine learning[J]. Mathematical Geosciences, 2023, 55(6): 799-828.
[52]	ABDI H, WILLIAMS L J. Principal component analysis[J]. WIREs Computational Statistics, 2010, 2(4): 433-459.
[53]	STRACKE A, WILLIG M, GENSKE F, et al. Chemical geodynamics insights from a machine learning approach[J]. Geochemistry, Geophysics, Geosystems, 2022, 23(10): e2022GC010606.
[54]	LI X M, ZHANG Y X, LI Z K, et al. Discrimination of Pb-Zn deposit types using sphalerite geochemistry: new insights from machine learning algorithm[J]. Geoscience Frontiers, 2023, 14(4): 101580.
[55]	HU B, ZENG L P, LIAO W, et al. The origin and discrimination of high-Ti magnetite in magmatic-hydrothermal systems: insight from machine learning analysis[J]. Economic Geology, 2022, 117(7): 1613-1627.
[56]	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. DOI
[57]	CRACKNELL M J, READING A M. Geological mapping using remote sensing data: a comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information[J]. Computers and Geosciences, 2014, 63: 22-33.
[58]	RECANATI A, GROZAVU N, BENNANI Y, et al. Apatite (U-Th-Sm)/He date dispersion: first insights from machine learning algorithms[J]. Earth and Planetary Science Letters, 2021, 554: 116655.
[59]	KUWATANI T, NAGATA K, OKADA M, et al. Machine-learning techniques for geochemical discrimination of 2011 Tohoku tsunami deposits[J]. Scientific Reports, 2014, 4: 7077. DOI PMID
[60]	LI X Y, ZHANG C. Machine learning thermobarometry for biotite-bearing magmas[J]. Journal of Geophysical Research: Solid Earth, 2022, 127(9): e2022JB024137.
[61]	PEDREGOSA F, VAROQUAUX G, GRAMFORT A, et al. Scikit-learn: machine learning in Python[J]. Journal of Machine Learning Research, 2011, 12: 2825-2830.
[62]	HASTIE T, TIBSHIRANI R, FRIEDMAN J. The elements of statistical learning: data mining, inference, and prediction[M]. New York: Springer, 2001.
[63]	SOKOLOVA M, LAPALME G. A systematic analysis of performance measures for classification tasks[J]. Information Processing and Management, 2009, 45(4): 427-437.
[64]	UEKI K, HINO H, KUWATANI T. Geochemical discrimination and characteristics of magmatic tectonic settings: a machine-learning-based approach[J]. Geochemistry, Geophysics, Geosystems, 2018, 19(4): 1327-1347.
[65]	ZHONG R C, DENG Y, LI W B, et al. Revealing the multi-stage ore-forming history of a mineral deposit using pyrite geochemistry and machine learning-based data interpretation[J]. Ore Geology Reviews, 2021, 133: 104079.
[66]	PETRELLI M, PERUGINI D. Solving petrological problems through machine learning: the study case of tectonic discrimination using geochemical and isotopic data[J]. Contributions to Mineralogy and Petrology, 2016, 171(10): 81.
[67]	王瑀, 邱昆峰, 侯照亮, 等. 石英Ti/Ge-P: 基于机器学习的矿床类型判别新图解[J]. 岩石学报, 2022, 38(1): 281-290.
[68]	WANG Y, QIU K F, MÜLLER A, et al. Machine learning prediction of quartz forming-environments[J]. Journal of Geophysical Research: Solid Earth, 2021, 126(8): e2021JB021925.
[69]	郭鹏. 机器学习揭示玄武岩构造背景与源区性质[J]. 矿物岩石地球化学通报, 2023, 42(1): 26-33, 6.
[70]	ROUSSEEUW P J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis[J]. Journal of Computational and Applied Mathematics, 1987, 20: 53-65.
[71]	周统, 邱昆峰, 王瑀, 等. 磷灰石Eu/Y-Ce: 基于大数据的源区类型判别新图解[J]. 岩石学报, 2022, 38(1): 291-299.
[72]	ROBERT C. Machine learning, a probabilistic perspective[J]. Chance, 2014, 27(2): 62-63.

基于机器学习的埃达克质岩构造背景判别研究

Machine learning-based approach for adakitic rocks tectonic setting determination

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