基于半监督神经网络的铜矿预测方法

徐永洋; 李孜轩; 谢忠; 冯斌; 陈浩

doi:10.3799/dqkx.2020.297

Volume 45 Issue 12

Dec. 2020

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Xu Yongyang, Li Zixuan, Xie Zhong, Feng Bin, Chen Hao, 2020. Prediction of Copper Mineralization Based on Semi-Supervised Neural Network. Earth Science, 45(12): 4563-4573. doi: 10.3799/dqkx.2020.297

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Xu Yongyang, Li Zixuan, Xie Zhong, Feng Bin, Chen Hao, 2020. Prediction of Copper Mineralization Based on Semi-Supervised Neural Network. Earth Science, 45(12): 4563-4573. doi: 10.3799/dqkx.2020.297

Xu Yongyang, Li Zixuan, Xie Zhong, Feng Bin, Chen Hao, 2020. Prediction of Copper Mineralization Based on Semi-Supervised Neural Network. Earth Science, 45(12): 4563-4573. doi: 10.3799/dqkx.2020.297

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Prediction of Copper Mineralization Based on Semi-Supervised Neural Network

doi: 10.3799/dqkx.2020.297

Xu Yongyang^{1, 2
,
,},
Li Zixuan¹,
Xie Zhong^{1, 2
,
,},
Feng Bin^{1, 3},
Chen Hao⁴

1.
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
2.
National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430078, China
3.
Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, Langfang 065000, China
4.
Wuhan Zondy Cyber Technology Co. Ltd., Wuhan 430074, China

Received Date: 2020-07-20
Publish Date: 2020-12-15

Abstract

Abstract

The introduction of artificial intelligence technology into metallogenic prediction can improve the prediction efficiency and research the hidden information between the exploration data and the results. In this paper, it designs a brand new semi-supervised neural network structure based on isolation forest with split-selection criterion(SCiForest) and autoencoder, using the advantage of semi-supervised learning method that the samples are easy to construct and the outstanding application effect in anomaly recognition. Based on the geochemical element data of Gangdise area in Tibet, the copper deposits in the study area is predicted. The prediction results are basically within the known mining area, which shows that the neural network structure in this paper can be used to predict the metallogenic prospective area.
- copper deposit,
- metallogenic prediction,
- deep learning,
- semi-supervised neural network

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References(35)

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Prediction of Copper Mineralization Based on Semi-Supervised Neural Network

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