3D metallogenic prediction based on machine learning: A case study of the Lala copper deposit in Sichuan Province

XIANG Jie; CHEN Jianping; XIAO Keyan; LI Shi; ZHANG Zhiping; ZHANG Ye

2019 Vol. 38, No. 12

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Article navigation > Geological Bulletin of China > 2019 > 38(12): 2010-2021

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XIANG Jie, CHEN Jianping, XIAO Keyan, LI Shi, ZHANG Zhiping, ZHANG Ye. 3D metallogenic prediction based on machine learning: A case study of the Lala copper deposit in Sichuan Province[J]. Geological Bulletin of China, 2019, 38(12): 2010-2021.

Citation:

XIANG Jie, CHEN Jianping, XIAO Keyan, LI Shi, ZHANG Zhiping, ZHANG Ye. 3D metallogenic prediction based on machine learning: A case study of the Lala copper deposit in Sichuan Province[J]. Geological Bulletin of China, 2019, 38(12): 2010-2021.

3D metallogenic prediction based on machine learning: A case study of the Lala copper deposit in Sichuan Province

1.
MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, CAGS, Beijing 100037, China
2.
School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
3.
National Joint Engineering Research Center for Shale Gas Exploration and Development, Chongqing Institute of Geology and Mineral Resources, Chongqing 401120, China

More Information

Corresponding author: CHEN Jianping, 3s@cugb.edu.cn

Received Date: 23 April 2019

Revised Date: 16 July 2019

Publish Date: 15 December 2019

Abstract

Abstract

Under the background of the vigorous development of big data, the quantitative prediction of mineral resources is the core part of geological big data. The basic idea of comprehensive analysis and mining of multi-information coincides with the concept of big data. With the Lala copper deposit as the study area, the authors carried out 3D mineral resources prediction based on machine learning. In this paper, 3D geological model was established to extract useful information of mineralization and build the quantitative prediction model of the study area. By using the "cube prediction model" prospecting method, the authors adopted the random forest algorithm of machine learning to calculate the probability distribution of mineralization in the study area. In this way, five prospecting prospective areas were delineated. The results show that the random forest has higher prediction accuracy and stability and can make quantitative evaluation on the importance of ore controlling factors. This study has successfully applied machine learning to the 3D mineral resources prediction and made a positive exploration for the prediction and evaluation of mineral resources in the future.
- mineral prediction /
- 3D modelling /
- machine learning /
- random forest /
- Lala copper deposit

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References

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