Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images

Abstract

Segmentation of 3D micro-Computed Tomographic (μCT) images of rock samples is essential for further Digital Rock Physics (DRP) analysis, however, conventional methods such as thresholding and watershed segmentation are susceptible to user-bias. Deep Convolutional Neural Networks (CNNs) have produced accurate pixelwise semantic (multi-category) segmentation results with natural images and μCT rock images, however, physical accuracy is not well documented. The performance of 4 CNN architectures is tested for 2D and 3D cases in 10 configurations. Manually segmented μCT images of Mt. Simon Sandstone guided by QEMSCANs are treated as ground truth and used as training and validation data, with a high voxelwise accuracy (over 99%) achieved. Downstream analysis is used to validate physical accuracy. The topology of each mineral is measured, the pore space absolute permeability and single/mixed wetting multiphase flow is modelled with direct simulation. These physical measures show high variance, with models that achieve 95%+ in voxelwise accuracy possessing permeabilities and connectivities orders of magnitude off. A network architecture is introduced as a hybrid fusion of U-Net and ResNet, combining short and long skip connections in a Network-in-Network configuration, which overall outperforms U-Net and ResNet variants in some minerals, while outperforming SegNet in all minerals in voxelwise and physical accuracy measures. The network architecture and the dataset volume fractions influence accuracy trade-off since sparsely occurring minerals are over-segmented by lower accuracy networks such as SegNet at the expense of under-segmenting other minerals which can be alleviated with loss weighting. This is an especially important consideration when training a physically accurate model for segmentation.