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Lithology Identification Comparison on Geophysical Logging Data of In-situ Leaching Uranium Based on Multiple Machine Learning Algorithms
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Yuhan ZOU1, Qinci LI1, 2, Weimin QUE1, 2, Liang HUANG3, Tongpan WU4, Zhiming DU1, 2, Zhenjiao JIANG3, Xuanyi CHEN3
Uranium Mining and Metallurgy | 2025, 44(2) : 9 - 17
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Uranium Mining and Metallurgy | 2025, 44(2): 9-17
• MINING AND HYDROMETALLURGY •
Lithology Identification Comparison on Geophysical Logging Data of In-situ Leaching Uranium Based on Multiple Machine Learning Algorithms
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Yuhan ZOU1, Qinci LI1, 2, Weimin QUE1, 2, Liang HUANG3, Tongpan WU4, Zhiming DU1, 2, Zhenjiao JIANG3, Xuanyi CHEN3
Affiliations
  • 1 Beijing Research Institute of Chemical Engineering and Metallurgy, CNNC, Beijing 101149, China
  • 2 China Nuclear Mining Science and Technology Corporation, CNNC, Beijing 101149, China
  • 3 Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130015, China
  • 4 College of Resources, Environment and Safety Engineering, University of South China, Hengyang 421001, China
Published: 2025-05-20 doi: 10.13426/j.cnki.yky.2024.10.09
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Machine learning algorithms can automatically learn and extract features from a large amount of geological data to achieve fast and accurate lithology identification. In this paper, the logging data of several wells in a sandstone-type uranium deposit in Inner Mongolia were randomly divided into training sets and verification sets according to the ratio of 7∶2. The model structure was adjusted and the hyperparameters were optimized for training. BC1401, BC2802, BC4603 and BC7206 well were used for testing to realize the comparative analysis of 5 kinds of models, such as random forest, XGBoost, K value proximity algorithm, BP neural network and SMOTE-LSTM algorithm. The results show that SMOTE-LSTM model has the most superior stability and accuracy, with an accuracy of 84.6%.

in-situ leaching of uranium  /  machine learning  /  sandstone-type uranium deposit  /  lithology identification  /  logging data  /  SMOTE-LSTM model  /  XGBoost model
Yuhan ZOU, Qinci LI, Weimin QUE, Liang HUANG, Tongpan WU, Zhiming DU, Zhenjiao JIANG, Xuanyi CHEN. Lithology Identification Comparison on Geophysical Logging Data of In-situ Leaching Uranium Based on Multiple Machine Learning Algorithms[J]. Uranium Mining and Metallurgy, 2025 , 44 (2) : 9 -17 . DOI: 10.13426/j.cnki.yky.2024.10.09
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doi: 10.13426/j.cnki.yky.2024.10.09
  • Receive Date：2024-10-18
  • Online Date：2025-07-04
  • Published：2025-05-20
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  • Received：2024-10-18
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Affiliations
    1 Beijing Research Institute of Chemical Engineering and Metallurgy, CNNC, Beijing 101149, China
    2 China Nuclear Mining Science and Technology Corporation, CNNC, Beijing 101149, China
    3 Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130015, China
    4 College of Resources, Environment and Safety Engineering, University of South China, Hengyang 421001, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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