Satellite-Based Deep Learning Models for Mineral Exploration

Abstract

Conventional methods, including quantification of evidence and fuzzy logic, are not sufficient to eliminate the complex nonlinear correlations present in geological data sets obtained from various sources The integration of satellite data into the automation of mineral exploration is important for modern techniques that optimise the detection of promising ore deposits in conditions of resource depletion, growing demand for materials and the need to ensure economic stability. This paper presents deep learning models such as convolutional neural networks (CNNs) for automated detection of copper porphyry deposits based on satellite imagery. The framework employs Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery with eight mineral alteration bands, integrated with 108 known deposit sites from Australian porphyry copper regions. Our baseline CNN achieved 91.0% classification accuracy and 0.93 area under the receiver operating characteristic curve (AUC-ROC) on test data. A comparative analysis of seven deep learning architectures showed that the Attention-CNN model outperformed others, achieving 93.0% accuracy and 0.95 AUC-ROC. This demonstrates that deep learning can effectively capture spatial-spectral patterns, offering a scalable solution for prospectivity mapping while reducing exploration costs and environmental impact.

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