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Machine learning strategies for lithostratigraphic classification based on geochemical sampling data: A case study in area of Chahanwusu River, Qinghai Province, China

基于机器学习的地球化学采样下伏基岩类型判别—以青海省察汗乌苏河地区为例

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Abstract

Based on the complex correlation between the geochemical element distribution patterns at the surface and the types of bedrock and the powerful capabilities in capturing subtle of machine learning algorithms, four machine learning algorithms, namely, decision tree (DT), random forest (RF), XGBoost (XGB), and LightGBM (LGBM), were implemented for the lithostratigraphic classification and lithostratigraphic prediction of a quaternary coverage area based on stream sediment geochemical sampling data in the Chahanwusu River of Dulan County, Qinghai Province, China. The local Moran’s I to represent the features of spatial autocorrelations, and terrain factors to represent the features of surface geological processes, were calculated as additional features. The accuracy, precision, recall, and F1 scores were chosen as the evaluation indices and Voronoi diagrams were applied for visualization. The results indicate that XGB and LGBM models both performed well. They not only obtained relatively satisfactory classification performance but also predicted lithostratigraphic types of the Quaternary coverage area that are essentially consistent with their neighborhoods which have the known types. It is feasible to classify the lithostratigraphic types through the concentrations of geochemical elements in the sediments, and the XGB and LGBM algorithms are recommended for lithostratigraphic classification.

摘要

基岩类型的判别是地质调查中十分重要的内容, 也是开展油气勘探和矿产勘探的重要基础工作。 本文采用决策树、随机森林、XGBoost、LightGBM 四种机器学习的方法, 实现了基于地球化学采样数 据的基岩类型判别。以15 种地球化学元素含量及其局部空间自相关莫兰指数和地形因子为特征, 训 练了不同的分类模型, 通过10 折交叉验证对模型做出了验证与评价。结果表明, 集成学习算法的分 类效果优于决策树, 其中XGBoost 和LightGBM 表现最好, 对复杂的高维空间数据和不平衡数据有较 强的处理能力。此外, 本文通过构建的分类模型成功地实现了对第四系沉积物下伏基岩类型的预测, Voronoi 图可视化结果表明, 预测基岩类型与其周围真实基岩类型基本吻合, 能初步划出基岩类型的 分界线。因此, 利用地球化学采样数据来判别其下伏基岩类型是可行的。

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Acknowledgments

The authors would like to thank the Co-Construction MapGIS Library by Engineering Research Center for Geographic Information System of China and Central South University for providing MapGIS® software. We also thank senior engineer Professor ZHANG Shao-ning (The 8th Team of Qinghai Provincial Bureau of Nonferrous Metals and Geological Exploration) and Professor LAI Jianqing (Central South University) for their kind assistance in the area of data collection.

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Authors and Affiliations

  1. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), Central South University, Changsha, 410083, China

    Bao-yi Zhang  (张宝一), Man-yi Li  (李曼懿), Wei-xia Li  (李伟霞), Zheng-wen Jiang  (蒋正文), Umair Khan, Li-fang Wang  (王丽芳) & Fan-yun Wang  (汪凡云)

  2. School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China

    Bao-yi Zhang  (张宝一), Man-yi Li  (李曼懿), Wei-xia Li  (李伟霞), Zheng-wen Jiang  (蒋正文), Umair Khan, Li-fang Wang  (王丽芳) & Fan-yun Wang  (汪凡云)

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  1. Bao-yi Zhang  (张宝一)
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  2. Man-yi Li  (李曼懿)
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  6. Li-fang Wang  (王丽芳)
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Correspondence to Li-fang Wang  (王丽芳) or Fan-yun Wang  (汪凡云).

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Foundation item

Projects(41772348, 42072326) supported by the National Natural Science Foundation of China; Project(2017YFC0601503) supported by the National Key Research and Development Program, China

Contributors

The overarching research goals were developed by ZHANG Bao-yi; WANG Li-fang provided the data curation and completed data preprocessing; LI Man-yi and LI Wei-xia trained the lithostratigraphic classifiers based on machine learning and predicted lithostratigraphic types underlying the Quaternary coverages; WANG Fun-yun analyzed and verified the results; JIANG Zheng-wen and Umair KHAN realized the visualization. The initial draft of the manuscript was written by ZHANG Bao-yi and LI Man-yi; WANG Li-fang replied to reviewers’ comments and revised the final version.

Conflict of interest

ZHANG Bao-yi, LI Man-yi, LI Wei-xia, JIANG Zheng-wen, Umair KHAN, WANG Li-fang, WANG Fan-yun declare that they have no conflict of interest.

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Zhang, By., Li, My., Li, Wx. et al. Machine learning strategies for lithostratigraphic classification based on geochemical sampling data: A case study in area of Chahanwusu River, Qinghai Province, China. J. Cent. South Univ. 28, 1422–1447 (2021). https://doi.org/10.1007/s11771-021-4707-9

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  • DOI: https://doi.org/10.1007/s11771-021-4707-9

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