Mississippi Valley-type (MVT) Pb-Zn mineralization is a key economic resource, yet its exploration is challenging due to complex alteration patterns and high costs. This study integrates ASTER satellite imagery with deep learning to enhance prospectivity mapping. We applied image processing techniques, including Principal Component Analysis (PCA), Band Ratios (BR), Band Math (BM), and Spectral Angle Mapper (SAM), to identify alteration zones. The Minimum Distance Classification (MDC) method classified these zones, extracting key evidence layers. These layers—dolomitization (MDC-PCA, SAM) and carbonate-iron oxide (MDC-BR, MDC-BM)—were integrated using Deep Autoencoder (DAE) and Fuzzy Logic Modeling (GFO) to generate prospectivity maps. Prediction-area (P-A) plots showed the DAE model outperformed GFO, achieving a normalized density (Nd) of 4.1 compared to 3.61 for GFO, indicating a more precise delineation of high-potential mineralization zones. Field validation confirmed strong alignment with known Pb-Zn occurrences. This study highlights the effectiveness of remote sensing and deep learning in cost-effective mineral exploration and provides a scalable framework for similar metallogenic provinces.
