Recent Advances and Future Perspectives of AI-Based Mineral Exploration: A Review of Machine Learning, Deep Learning, and Geologically Informed Approaches
- 1Department of Earth and Environmental Sciences, Chungbuk National University, Cheongju 28644, Republic of Korea
- 2Ocean Georesources Research Department, Korea Institute of Ocean Science and Technology (KIOST), Busan 49111, Republic of Korea
Abstract
Driven by the energy transition and carbon-neutrality targets, global demand for critical minerals is increasing rapidly, while the discovery of new mineral deposits has become increasingly challenging because easily detectable outcropping deposits are being depleted, and exploration is shifting toward concealed ore systems. In this context, data-driven approaches based on machine learning (ML) and deep learning (DL) are increasingly complementing conventional geological, geochemical, geophysical, and remote-sensing methods. This review provides a structured synthesis of AI-based mineral exploration studies published over the past decade, focusing on four key aspects: theoretical foundations; applications to diverse exploration datasets, including remote sensing, geochemistry, geophysics, and drill-core imagery; advances in mineral prospectivity mapping (MPM); and emerging trends and challenges, such as limited labeled data, uncertainty quantification, geological consistency, explainability, physics-informed neural networks (PINNs), and the adaptation of foundation models to geoscience data. Convolutional neural networks, autoencoders, generative adversarial networks, Transformers, and graph neural networks show strong potential for improving pattern recognition, data integration, and workflow automation. Overall, AI-based exploration is expected to play an increasingly important role in detecting concealed mineral deposits and strengthening resilient critical-mineral supply chains.
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BibTeX
@article{lee2026recent,
title = {{Recent Advances and Future Perspectives of AI-Based Mineral Exploration: A Review of Machine Learning, Deep Learning, and Geologically Informed Approaches}},
author = {Seungyeol Lee and Inkyeong Moon},
journal = {Minerals},
year = {2026},
month = may,
doi = {10.3390/min16060584},
url = {https://doi.org/10.3390/min16060584},
note = {CC BY},
}
RIS
TY - JOUR
TI - Recent Advances and Future Perspectives of AI-Based Mineral Exploration: A Review of Machine Learning, Deep Learning, and Geologically Informed Approaches
AU - Seungyeol Lee
AU - Inkyeong Moon
PY - 2026
DA - 2026/05/29
JO - Minerals
DO - 10.3390/min16060584
UR - https://doi.org/10.3390/min16060584
AB - Driven by the energy transition and carbon-neutrality targets, global demand for critical minerals is increasing rapidly, while the discovery of new mineral deposits has become increasingly challenging because easily detectable outcropping deposits are being depleted, and exploration is shifting toward concealed ore systems. In this context, data-driven approaches based on machine learning (ML) and deep learning (DL) are increasingly complementing conventional geological, geochemical, geophysical, and remote-sensing methods. This review provides a structured synthesis of AI-based mineral exploration studies published over the past decade, focusing on four key aspects: theoretical foundations; applications to diverse exploration datasets, including remote sensing, geochemistry, geophysics, and drill-core imagery; advances in mineral prospectivity mapping (MPM); and emerging trends and challenges, such as limited labeled data, uncertainty quantification, geological consistency, explainability, physics-informed neural networks (PINNs), and the adaptation of foundation models to geoscience data. Convolutional neural networks, autoencoders, generative adversarial networks, Transformers, and graph neural networks show strong potential for improving pattern recognition, data integration, and workflow automation. Overall, AI-based exploration is expected to play an increasingly important role in detecting concealed mineral deposits and strengthening resilient critical-mineral supply chains.
SN - 2075-163X
ER -