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Research Progress on Lithologic Logging Evaluation of Uranium Ore Layers Based on Machine Learning
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Kun XIAO1, Chang-wei JIAO1, *, Ya-xin YANG1, Xiao HUANG2, Dian-xue WANG2, Zhong-yi DUAN1, Yi-chen XU1
Science Technology and Engineering | 2025, 25(12) : 4827 - 4839
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Science Technology and Engineering | 2025, 25(12): 4827-4839
• Surveies·Astronomy and Geosciences •
Research Progress on Lithologic Logging Evaluation of Uranium Ore Layers Based on Machine Learning
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Kun XIAO1, Chang-wei JIAO1, *, Ya-xin YANG1, Xiao HUANG2, Dian-xue WANG2, Zhong-yi DUAN1, Yi-chen XU1
Affiliations
  • 1 Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China
  • 2 Nuclear Industry Group No.243, Chifeng 024000, China
Published: 2025-04-28 doi: 10.12404/j.issn.1671-1815.2402216
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In recent years, artificial intelligence has demonstrated strong pattern recognition and classification capabilities across various fields, providing new insights for lithology identification. Starting from three methods: support vector machines, neural networks, and ensemble learning, the basic principles, advantages and disadvantages of these machine learning algorithms were reviewed, as well as their research progress and application in the field of uranium ore bed lithology identification. The results show that machine learning can effectively identify the correlation between logging data and different lithologies through model training, transforming the process of lithology identification into a machine learning process. This can greatly improve the automation level and accuracy of lithology identification, holding significant practical importance and a broad development prospect.

uranium deposit  /  lithology identification  /  machine learning  /  classification problem  /  logging evaluation
Kun XIAO, Chang-wei JIAO, Ya-xin YANG, Xiao HUANG, Dian-xue WANG, Zhong-yi DUAN, Yi-chen XU. Research Progress on Lithologic Logging Evaluation of Uranium Ore Layers Based on Machine Learning[J]. Science Technology and Engineering, 2025 , 25 (12) : 4827 -4839 . DOI: 10.12404/j.issn.1671-1815.2402216
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Year 2025 volume 25 Issue 12
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Article Info
doi: 10.12404/j.issn.1671-1815.2402216
  • Receive Date：2024-03-28
  • Online Date：2025-07-09
  • Published：2025-04-28
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  • Received：2024-03-28
  • Revised：2025-01-21
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    1 Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China
    2 Nuclear Industry Group No.243, Chifeng 024000, 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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