Mapping distribution of fractures and minerals in rock samples using Res-VGG-UNet and threshold segmentation methods

Understanding the internal structures of rock and their subsequent evolution is essential across multiple disciplines. Fractures and mineral grains are significant components of internal structures. These components influence natural processes and human activities, such as oil migration and extraction. With advancements in non-destructive evaluation (NDE) techniques, especially X-ray computed tomography (CT), in-depth analyses of internal structures of rock have become feasible. However, the data-intensive nature of CT scans necessitates more efficient post-processing algorithms. This research uses the Residual Network-Visual Geometry GroupUNet (Res-VGG-UNet) deep learning framework in conjunction with two threshold segmentation methods to capture components like fractures, the drilled hole, and minerals. The performance of Res-VGG-UNet is evaluated against DeepLabv3+ and Weka3D. Key findings include the capability of the deep learning technique to distinguish between various components and the performance of single-slice-based versus multi-slice-based threshold segmentation methods. VGG16 and VGG19 outperform DeepLabv3+ and Weka3D in the performance of fracture classification. Moreover, a 3D reconstruction technique is introduced, providing enhanced insights into internal features of the rock. We conclude this paper by showing the potential to use the outcomes of the deep learning and multi-slice-based threshold segmentation methods to develop discrete element models, accurately reflecting the physical characteristics of rock specimens.