A foundation model for rock thin-section images analysis

Rock thin-section image analysis is a fundamental task in geological and mineralogical research. Traditional methods rely on visual inspection by experts using optical microscopes, which are inherently subjective, experience-dependent, and time-consuming. Here, we introduce RoImAI, a vision foundation model specifically designed for rock thin-section microscopy images, enabling rapid and precise rock segmentation, identification, and lithology report generation. A large-scale dataset of rock thin-section microscopy images comprising 30,336 images and approximately two million rock particles from 17 different regions was constructed to develop and validate RoImAI. RoImAI leverages Transformer-based deep learning techniques to achieve high-precision segmentation across multi-center datasets from diverse geological regions. RoImAI employs a hierarchical classification strategy to identify rock particles accurately. Furthermore, RoImAI outperforms human experts in efficiency and accuracy when generating structured lithology reports. The intelligent analytical capabilities and high accuracy of RoImAI strongly enable the automated processing of the rapidly expanding volume of rock thin-section images.