基于机器学习的矿物智能识别方法研究进展与展望

郝慧珍; 顾庆; 胡修棉

doi:10.3799/dqkx.2020.360

Volume 46 Issue 9

Oct. 2021

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Article Navigation > Earth Science > 2021 > 46(9): 3091-3106

Hao Huizhen, Gu Qing, Hu Xiumian, 2021. Research Advances and Prospective in Mineral Intelligent Identification Based on Machine Learning. Earth Science, 46(9): 3091-3106. doi: 10.3799/dqkx.2020.360

Citation:

Hao Huizhen, Gu Qing, Hu Xiumian, 2021. Research Advances and Prospective in Mineral Intelligent Identification Based on Machine Learning. Earth Science, 46(9): 3091-3106. doi: 10.3799/dqkx.2020.360

Hao Huizhen, Gu Qing, Hu Xiumian, 2021. Research Advances and Prospective in Mineral Intelligent Identification Based on Machine Learning. Earth Science, 46(9): 3091-3106. doi: 10.3799/dqkx.2020.360

Citation:

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Research Advances and Prospective in Mineral Intelligent Identification Based on Machine Learning

doi: 10.3799/dqkx.2020.360

Hao Huizhen^{1, 2
,
,},
Gu Qing^{1, 3},
Hu Xiumian^{4
,
,}

1.
Software Institute, Nanjing University, Nanjing 210023, China
2.
School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China
3.
Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China
4.
School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China

Received Date: 2020-09-16
Available Online: 2021-10-14
Publish Date: 2021-10-14

Abstract

Abstract

Mineral intelligent identification is a developing interdisciplinary research field between earth science and information science, where machine learning shows great vitality. This paper divides the procedure of mineral intelligent identification into four stages, including mineral collection, data acquisition, model building and category discriminant. Based on the test methods and data types, the mineral intelligent identification can be achieved by three different research routes, namely, chemical-composition-based, microscopic-optical-image-based and spectral-image-based. Various methods of machine learning for mineral intelligent recognition are reviewed in detail including statistical learning, similarity measurement, decision tree, artificial neural network and few new technologies related to testing sample. We suggest that the future directions in this field are to eliminate the gap between geology and artificial intelligence, to build high-quality mineral datasets that can be learned by the machine, to explore and consummate machine learning methods suitable for mineral intelligence identification, to increase the ability of model explanation, and to strengthen the practice of industrial application.
- machine learning,
- mineral identification,
- mineralogy,
- artificial intelligence,
- geology

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References(87)

References

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