Advances in the application of machine learning methods in mineral prospectivity mapping

Ma Yao; Zhao Jiangnan

doi:10.19509/j.cnki.dzkq.2021.0108

Volume 40 Issue 1

Jan. 2021

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Ma Yao, Zhao Jiangnan. Advances in the application of machine learning methods in mineral prospectivity mapping[J]. Bulletin of Geological Science and Technology, 2021, 40(1): 132-141. doi: 10.19509/j.cnki.dzkq.2021.0108

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Ma Yao, Zhao Jiangnan. Advances in the application of machine learning methods in mineral prospectivity mapping[J]. Bulletin of Geological Science and Technology, 2021, 40(1): 132-141. doi: 10.19509/j.cnki.dzkq.2021.0108

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Advances in the application of machine learning methods in mineral prospectivity mapping

doi: 10.19509/j.cnki.dzkq.2021.0108

Ma Yao,
Zhao Jiangnan^,

Received Date: 31 Oct 2019

Abstract

Abstract

This paper reviews the development of mineral prospectivity mapping at home and abroad, and conducts statistical comparative analysis of relevant foreign literature in the past decade.It shows that machine learning methods have become a hot topic in the field of mineral prospectivity mapping, and have played an active role in the following three aspects: ① extraction and mining of hidden and unrecognizable mineralization information in complex data; ② association and transformation of ore-forming anomaly information; ③ fusion, prediction and discovery of ore-forming anomaly information from multi-source geological data.Firstly, the application effects of major machine learning algorithms and models, such as logistic regression, artificial neural networks, random forests, and support vector machines, in mineral prospectivity mapping are reviewed.Secondly, it discusses the main problems in the application process, such as sample selection, misclassification cost, uncertainty evaluation, and model performance evaluation, as well as the current solutions.Finally, it is proposed that quantitative prediction of mineral resources based on big data and machine learning is an important trend in the future.
- mineral prospectivity mapping,
- big data of geosciences,
- data mining,
- information integration,
- machine learning

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References

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