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Accurate segmentation of digital rock images is essential for characterizing pore–matrix systems and predicting petrophysical properties. However, the diversity of rock textures across different lithologies poses a significant challenge for conventional segmentation networks, especially under limited training data. To address this, we introduce DRI-SAM (Digital Rock Image – Segment Anything Model), a hybrid segmentation framework that leverages the powerful visual prior of the Segment Anything Model (SAM) and adapts it to the digital rock domain. Specifically, we apply LoRA-based fine-tuning to SAM’s image encoder to better capture rock-specific microstructures, while U-Net is employed to generate prompt points, guiding SAM toward accurate pore–matrix delineation. This approach retains the encoder’s representational power while allowing domain-specific adaptation via LoRA, enabling effective cross-domain generalization under limited training data. The model is trained exclusively on 200 annotated images of Bentheimer sandstone, covering two distinct voxel resolutions, and is evaluated on digital rock images of varying lithologies, resolutions and imaging modalities. The results confirm that DRI-SAM achieves accurate segmentation on both sandstone and more challenging carbonate samples, including synthetic and SEM images, without additional retraining or parameter adjustments. Compared to DeepLabV3+ and the only LoRA-tuned SAM, DRI-SAM demonstrates superior performance under limited supervision, highlighting its strong generalization and practical value in digital rock image analysis. Moreover, the findings suggest that foundation models like SAM, when properly adapted, also hold great promise for broader geoscientific imaging tasks.
Wang et al. (Wed,) studied this question.