Pore-scale digital images are usually obtained from micro computed tomography (micro-CT) data that has been segmented into void and grain space. Image segmentation is a critical process for digital rock analyses that can influence pore-scale characterisation studies and/or the numerical simulation of petrophysical properties. This project is to study on the sandstone micro-CT image segmentation by using convolutional neural network.

A Diverse Super Resolution Dataset of Digital Rocks (DeepRockSR): Sandstone, Carbonate, and Coal. This dataset contains an organised and processed collection of greyscale digital rock images for the purpose of image super resolution training. In total, there are 12,000 2D images and 3,000 3D volumes contained in this dataset. Sandstone: Bentheimer Sandstone 1 https://www.digitalrocksportal.org/projects/211 Bentheimer Sandstone 2 https://www.digitalrocksportal.org/projects/135 Berea Sandstone https://www.digitalrocksportal.org/projects/135 Leopard Sandstone https://www.digitalrocksportal.org/projects/135 Gildehauser Sandstone https://www.digitalrocksportal.org/projects/134 Wilcox Tight Sandstone https://www.digitalrocksportal.org/projects/6 Carbonate: Estaillades Carbonate https://www.digitalrocksportal.org/projects/58...