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Mineral prospectivity mapping for multi-source geoscience data: A novel unsupervised deep learning method
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DOI：10.1016/j.oregeorev.2025.106866.png)
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Abstract
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• This paper proposes an unsupervised deep learning method based on vision Transformer for mineral prospectivity mapping. • The proposed method mines geochemical and geological information from multi-source geoscience data to improve the accuracy of identifying high-mineralization potential areas. • This method trains the model in an unsupervised way, avoiding problems such as data classification imbalance and the absence of data labels.
Keywords:
Mineral prospectivity mapping
Geoscience data
Unsupervised learning
Deep learning
Vision Transformer
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