基于机器学习的绿泥石微量元素判别矿床类型

侯霖莉; 吴松; 易建洲; 次琼; 陈烈; 刘晓峰; 魏守才; 阿旺旦增; 郑有业; 刘鹏

doi:10.3799/dqkx.2023.173

Volume 49 Issue 12

Dec. 2024

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Article Navigation > Earth Science > 2024 > 49(12): 4303-4317

Hou Linli, Wu Song, Yi Jianzhou, Ci Qiong, Chen Lie, Liu Xiaofeng, Wei Shoucai, A Wang Danzeng, Zheng Youye, Liu Peng, 2024. Discriminating Deposit Types Using Chlorite Trace Elements Based on Machine Learning. Earth Science, 49(12): 4303-4317. doi: 10.3799/dqkx.2023.173

Citation:

Hou Linli, Wu Song, Yi Jianzhou, Ci Qiong, Chen Lie, Liu Xiaofeng, Wei Shoucai, A Wang Danzeng, Zheng Youye, Liu Peng, 2024. Discriminating Deposit Types Using Chlorite Trace Elements Based on Machine Learning. Earth Science, 49(12): 4303-4317. doi: 10.3799/dqkx.2023.173

Citation:

Hou Linli, Wu Song, Yi Jianzhou, Ci Qiong, Chen Lie, Liu Xiaofeng, Wei Shoucai, A Wang Danzeng, Zheng Youye, Liu Peng, 2024. Discriminating Deposit Types Using Chlorite Trace Elements Based on Machine Learning. Earth Science, 49(12): 4303-4317. doi: 10.3799/dqkx.2023.173

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Discriminating Deposit Types Using Chlorite Trace Elements Based on Machine Learning

doi: 10.3799/dqkx.2023.173

Hou Linli^{1
,
,},
Wu Song^{1
,
,
,},
Yi Jianzhou²,
Ci Qiong³,
Chen Lie⁴,
Liu Xiaofeng³,
Wei Shoucai³,
A Wang Danzeng³,
Zheng Youye^{1, 5},
Liu Peng¹

1.
School of Earth Science and Resources, China University of Geosciences, Beijing 100083, China
2.
Land and Mineral Rights Trading and Resource and Reserve Evaluation Centre of Tibet Autonomous Region, Lhasa 850000, China
3.
No.2 Geological Party, Bureau of Geology and Mineral Exploration and Development, Lhasa 850000, China
4.
No.5 Geological Party, Bureau of Geology and Mineral Exploration and Development, Lhasa 850000, China
5.
School of Earth Resources, China University of Geosciences, Wuhan 430074, China

Received Date: 2023-06-13
Available Online: 2025-01-09
Publish Date: 2024-12-25

Abstract

Abstract

In order to study whether chlorite trace elements can effectively classify different genetic deposit types, in this paper, 2 928 trace element data of chlorite from 13 different deposits were collected, which belong to three distinct genetic types, including porphyry, skarn and epithermal deposits.Three different machine learning algorithms, including random forest, support vector machine (SVM) and artificial neural network, were used to establish classification models for the genetic types of deposits and analyze the importance of characteristics.The results show that the SVM model based on Ni, Cr, Co, Sr, V and Zn, 6 trace elements have the best classification effect, the highest Kappa coefficient is 0.89, the weighted average score of precision, recall and F1 value reach 0.96, and Ni, V and Co are the three most critical discriminant elements. In this paper it fully confirms that the machine learning classification model based on chlorite mineral trace elements can discriminate deposit types and provide an important indicator for the rapid evaluation of regional scale prospecting.
- chlorite,
- trace element,
- genetic type of deposit,
- machine learning,
- mineral deposits

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References(51)

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