Mineral prospectivity mapping (MPM) is recognized as an essential tool for targeting new mineral deposits. MPM typically comprises two end-member approaches: knowledge-driven and data-driven. Knowledge-driven MPM relies on expert knowledge, which is based on causal relationships but is not readily adaptable to dynamic changes. Data-driven MPM is capable of identifying underlying data patterns but involves poorly interpretable decision logic. Combining the advantages of knowledge-driven and data-driven paradigms is a research frontier in MPM. In this study, we designed a data-knowledge dual-driven model coupling artificial intelligence (AI) with a mineral systems approach to MPM. This model can utilize mineral systems as a guideline for data-driven AI to reasonably implement data selection, proxy extraction, and model operation for MPM. The newly developed data-knowledge dual-driven model achieved superior predictive performance and offered better interpretability compared to pure data-driven MPM.
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Research Article|
December 30, 2024
Early Publication
A novel data-knowledge dual-driven model coupling artificial intelligence with a mineral systems approach for mineral prospectivity mapping
Renguang Zuo;
Renguang Zuo
1
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
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Fanfan Yang;
Fanfan Yang
2
School of Future Technology, China University of Geosciences, Wuhan 430074, China
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Qiuming Cheng;
Qiuming Cheng
3
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing 100083, China
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Oliver P. Kreuzer
Oliver P. Kreuzer
4
Economic Geology Research Centre (EGRU), College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia
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Renguang Zuo
1
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
Fanfan Yang
2
School of Future Technology, China University of Geosciences, Wuhan 430074, China
Qiuming Cheng
3
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing 100083, China
Oliver P. Kreuzer
4
Economic Geology Research Centre (EGRU), College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia
Publisher: Geological Society of America
Received:
24 Nov 2024
Revision Received:
11 Dec 2024
Accepted:
14 Dec 2024
First Online:
30 Dec 2024
Online ISSN: 1943-2682
Print ISSN: 0091-7613
© 2024 Geological Society of America
Geology (2024)
Article history
Received:
24 Nov 2024
Revision Received:
11 Dec 2024
Accepted:
14 Dec 2024
First Online:
30 Dec 2024
Citation
Renguang Zuo, Fanfan Yang, Qiuming Cheng, Oliver P. Kreuzer; A novel data-knowledge dual-driven model coupling artificial intelligence with a mineral systems approach for mineral prospectivity mapping. Geology 2024; doi: https://doi.org/10.1130/G52970.1
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