Abstract
Machine learning algorithms, including supervised and unsupervised learning ones, have been widely used in mineral prospectivity mapping. Supervised learning algorithms require the use of numerous known mineral deposits to ensure the reliability of the training results. Unsupervised learning algorithms can be applied to areas with rare or no known deposits. Reinforcement learning (RL) is a type of machine learning algorithm that differs from supervised and unsupervised learning models in that the learning process is performed interactively by the agent and environment. The environment feeds the agent with reward signals and states, and the agent synthetically evaluates the mineralization potential of each state based on these rewards. In this study, a deep RL framework was constructed for mineral prospectivity mapping, and a case study for mapping gold prospectivity in northwest Hubei Province, China, was used to test the framework. The deep RL agent extracted the information of known mineralization by automatically interacting with the environment while simultaneously mining potential mineralization information from the unlabeled dataset. Its comparison with random forest and isolation forest models demonstrates that deep RL performs better regardless of the number of known mineral deposits because of its unique reward and feedback mechanism. The delineated high-potential areas show a strong spatial correlation with known gold deposits and can therefore provide significant clues for future prospecting in the study area.
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Acknowledgements
We would like to thank two reviewers’ comments and suggestions which helped us improve this study. This study was supported by the IAMG Mathematical Geosciences Student Awards and the National Natural Science Foundation of China (42172326).
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Shi, Z., Zuo, R. & Zhou, B. Deep Reinforcement Learning for Mineral Prospectivity Mapping. Math Geosci 55, 773–797 (2023). https://doi.org/10.1007/s11004-023-10059-9
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DOI: https://doi.org/10.1007/s11004-023-10059-9