Leveraging Domain Expertise in Machine Learning for Critical Metal Prospecting in the Oslo Rift: A Case Study for Fe-Ti-P-Rare Earth Element Mineralization
Abstract
:1. Introduction
2. Geological Background of the Study Area
3. Data
4. Methodology and Workflow
4.1. Training Data Sampling Strategy
4.2. Feature Engineering
4.3. Feature Selection
4.4. Classification Model
5. Results and Discussion
5.1. Area 1 of 3: Kodal
5.2. Area 2 of 3: Siljan
5.3. Area 3 of 3: Contact between B1-Basalt and Monzonite/Syenites
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Wang, Y.; Coint, N.; Mansur, E.T.; Acosta-Gongora, P.; Miranda, A.C.R.; Nasuti, A.; Baranwal, V.C. Leveraging Domain Expertise in Machine Learning for Critical Metal Prospecting in the Oslo Rift: A Case Study for Fe-Ti-P-Rare Earth Element Mineralization. Minerals 2024, 14, 377. https://doi.org/10.3390/min14040377
Wang Y, Coint N, Mansur ET, Acosta-Gongora P, Miranda ACR, Nasuti A, Baranwal VC. Leveraging Domain Expertise in Machine Learning for Critical Metal Prospecting in the Oslo Rift: A Case Study for Fe-Ti-P-Rare Earth Element Mineralization. Minerals. 2024; 14(4):377. https://doi.org/10.3390/min14040377
Chicago/Turabian StyleWang, Ying, Nolwenn Coint, Eduardo Teixeira Mansur, Pedro Acosta-Gongora, Ana Carolina Rodrigues Miranda, Aziz Nasuti, and Vikas Chand Baranwal. 2024. "Leveraging Domain Expertise in Machine Learning for Critical Metal Prospecting in the Oslo Rift: A Case Study for Fe-Ti-P-Rare Earth Element Mineralization" Minerals 14, no. 4: 377. https://doi.org/10.3390/min14040377