Developing conditional generative adversarial networks can revolutionize geological modeling by directly predicting and translating between different reservoir properties such as facies, porosity, permeability, and water saturation from existing subsurface data. These generative AI frameworks overcome traditional conditioning limitations by learning complex spatial relationships and geological patterns, enabling automated property mapping that captures subsurface heterogeneity while maintaining geological realism and physical constraints. This approach transforms reservoir characterization workflows from manual, time-intensive processes to intelligent, data-driven systems that can seamlessly generate missing property distributions and enhance uncertainty quantification for improved reservoir management decisions.

Adapting large-scale AI foundation models like Segment Anything Model (SAM) for interactive geological interpretation, transforms how geoscientists approach complex data analysis across multiple domains. These models enable dual-mode capabilities where AI can perform automated broad-scale analysis of entire geological datasets while simultaneously allowing user-directed localized investigation through intuitive prompts and guidance. This paradigm shift bridges the gap between powerful general-purpose AI and domain-specific geological expertise, revolutionizing workflows from seismic facies segmentation and petrographic analysis to mineral identification and stratigraphic interpretation.

Traditional petrographic analysis faces a fundamental challenge: the trade-off between image resolution and field of view in microscope imaging. This limitation, combined with time-intensive manual analysis, restricts accurate quantitative characterization of rock properties. Our research focus on bridging the gap between traditional microscopy limitations and modern AI capabilities for enhanced geological analysis.

We apply state-of-the-art deep learning techniques to predict locations of valuable mineral deposits. By training neural networks on diverse geological, geochemical, and remote sensing datasets, our models learn complex patterns that traditional methods may miss. This allows us to generate high-resolution mineral prospectivity maps, improve exploration targeting, and reduce time and cost in identifying promising sites.