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Gold Science and Technology >

2023 , Vol. 31 >Issue 5: 721 - 735

DOI: https://doi.org/10.11872/j.issn.1005-2518.2023.05.063

Mineral Exploration and Resource Evaluation

ADASYN-CatBoost Method for Intelligent Identification of Logging Lithology Considering Unbalanced Data：A Case Study of Zhaoxian Gold Deposit in Northwestern Jiaodong Peninsula

  • Fangying XU , 1, 2 ,
  • Yanhong ZOU , 1, 2 ,
  • Zhuowei YI 1, 2 ,
  • Fuqiang YANG 1, 2 ,
  • Xiancheng MAO 1, 2
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  • 1. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha 410083, Hunan, China
  • 2. School of Geosciences and Info-Physics, Central South University, Changsha 410083, Hunan, China

Received date: 2023-04-24

  Revised date: 2023-06-30

  Online published: 2023-11-21

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Abstract

Logging lithology identification is helpful to quickly and accurately identify the underlying strata and rock mass in the overburden area，which is of great significance to the geological prospecting exploration of metal mines. Based on the actual logging data of the Zhaoxian gold deposit in the northwest of Jiaodong Peninsula，this paper combined machine learning methods to research on intelligent identification of lithology. In view of the diversity and non-equilibrium of lithology distribution of complex rock formations in the deposit，considering the strong non-linear relationship between logging response and lithology，this paper proposed an intelligent identification method for logging lithology based on ADASYN imbalanced data processing and CatBoost machine learning.Firstly，the ADASYN algorithm was used to process the unbalanced logging sample data and generate synthetic samples according to the weighted distribution of small class samples. Then，the CatBoost algorithm was used to construct a machine learning model between logging characteristic and lithology. The validation curve was used to determine the hyperparametric grid search range of the model. Parameters were optimized by combining grid search with grid search and 10-fold cross validation to establish the optimal lithology classification model.Finally，the performance of the model was evaluated by indices such as accuracy，recall and F1 score on the test set，while the results of the lithology classification were interpreted by the model output of the feature importance and the partial dependence map.An example was given on the logging data from the Zhaoxian gold deposit in northwest Jiaodong peninsula，the lithology identification and interpretation analysis were conducted on 10 types of lithologies based on sample data equalisation. The model evaluation results show that the accuracy，recall and F1 score on the test set reached 98.21%，98.20% and 98.20%，respectively.CatBoost lithology classification was compared with GBDT and LightGBM algorithms，and the results show that CatBoost classifier has the best performance and is superior to the lithology recognition effect of sample data without equalization processing.The comparison with the lithology of example logging section cores verifies the validity of the model classification results.The results of the feature importance of the model output indicate that the logging features contribute to lithology classification are resistivity，natural potential and natural gamma.The strong correlation between these logging features and the identification of the lithology is a good indication of further mineralization.

Key words： lithology identification; ADASYN-CatBoost; logging; unbalanced data; machine learning; Zhaoxian gold deposit

Cite this article

Fangying XU , Yanhong ZOU , Zhuowei YI , Fuqiang YANG , Xiancheng MAO . ADASYN-CatBoost Method for Intelligent Identification of Logging Lithology Considering Unbalanced Data：A Case Study of Zhaoxian Gold Deposit in Northwestern Jiaodong Peninsula[J]. Gold Science and Technology, 2023 , 31(5) : 721 -735 . DOI: 10.11872/j.issn.1005-2518.2023.05.063

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http://www.goldsci.ac.cn/article/2023/1005-2518/1005-2518-2023-31-5-721.shtml

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