Leveraging multispectral and hyperspectral imagery, we analyze fine-grained spectral signatures across geological materials to identify mineralogical variations and alteration zones. By integrating advanced preprocessing, spectral unmixing, and classification algorithms, our approach enhances the detection of subtle spectral features that are key to mapping lithology, mineral distribution, and diagenetic transformations. The insights gained aid in refining exploration models and improving scalability of remote sensing in reservoir characterization.

We integrate 3D laser scanning, photogrammetry, and drone-based imaging with spectral data to generate high-resolution digital outcrop models. This combination enables both geometric and compositional analysis of geological formations, capturing stratigraphy, fractures, facies, and mineralogical variations. By linking structural and spectral attributes, digital outcrop modeling provides powerful insights for reservoir characterization, diagenetic studies, and teaching applications.