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Gradient Boosting Decision Tree for Lithology Identification with Well Logs: A Case Study of Zhaoxian Gold Deposit, Shandong Peninsula, China

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Abstract

Identifying lithology from well logs is an important step in deep prospecting and resource estimation. Various machine learning algorithms have been adopted to identify lithology in oil and gas fields. Such algorithms, however, are rarely used for mineral deposits because of their complex geological conditions. In this paper, we propose an application framework using the gradient boosting decision tree (GBDT) algorithm to identify lithology from well logs in a mineral deposit. The GBDT classifier was built via the procedure of grid search and cross-validation to optimize the hyperparameters. In the Zhaoxian gold deposit, as the study area, an optimized GBDT classifier was built to fit the association between a set of well logs and ten lithological classes. The results demonstrate that the GBDT classifier has good classification performance in lithology identification, with a precision of 93.55%, a recall of 93.49% and an F1-score of 93.27%. The GBDT classification results also indicate that the major features contributing to the lithology classification are resistivity, followed by spontaneous potential and natural gamma according to the model interpretation of feature importance and partial dependence plots. The study demonstrates that the GBDT model can enhance our understanding of lithology identification from well logs in mineral deposits, which provides significant implications for further exploration targeting the deep-seated parts of mineral deposits.

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Code Availability

The relevant codes are written in Python. To access the source file of relevant data and codes, one can visit the repository on GitHub (https://github.com/orrange/Lithology-classification).

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Acknowledgements

The work in the paper was supported by the National Key Research and Development Program of China (Grant No. 2019YFC1805905), the National Natural Science Foundation of China (41872249, 41772349, 41972309) and the Fundamental Research Funds for the Central Universities of Central South University (2020zzts653).

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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

    Yanhong Zou, Yuting Chen & Hao Deng

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

    Yanhong Zou, Yuting Chen & Hao Deng

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  1. Yanhong Zou
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  2. Yuting Chen
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Correspondence to Yanhong Zou or Hao Deng.

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Zou, Y., Chen, Y. & Deng, H. Gradient Boosting Decision Tree for Lithology Identification with Well Logs: A Case Study of Zhaoxian Gold Deposit, Shandong Peninsula, China. Nat Resour Res 30, 3197–3217 (2021). https://doi.org/10.1007/s11053-021-09894-6

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