Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN), Part I: Super-resolution enhancement using a 3D CNN. (October 2022)

• Record Type: Journal Article
• Authors: Roslin, A.; Marsh, M.; Piché, N.; Provencher, B.; Mitchell, T.R.; Onederra, I.A.; Leonardi, C.R.
• Is Part Of: Minerals engineering. Volume 188(2022)
• Publication Date: 2022-10
• Subjects: Convolutional neural network -- Micro-CT -- Super-resolution -- Igneous rocks -- Deep learning -- U-net 3D; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2022.107748
• Languages: English
• ISSNs: 0892-6875

Abstract

Abstract: X-ray micro-computed tomography (micro-CT) is widely used for three-dimensional analysis of many rock types. However, the practical implementation of this method for micro-porous samples requires a compromise between the resolution of the images and the obtainable field of view (FOV). Generally, resolution enhancement results in a reduction of the FOV. The generation of high-quality micro-CT images is an expensive and time consuming task due to the competing requirements of a large FOV and fine resolution. To alleviate this, super-resolution processing, based on deep learning, is proposed to improve the quality of low-resolution images that can obtain a large FOV. In this research, a super-resolution technique employing the three-dimensional U-Net convolutional neural network (CNN) architecture was applied to enhance the resolution of granodiorite rock sample images. This was undertaken using two sets of micro-CT image triplexes, where the first triplex contained 3-, 6-, and 12-micron resolution sets, and the second triplex contained 1-, 2-, and 4-micron resolution sets. For each triplex, 80% of the images were used for training the neural network with the remaining 20% used for validation. Further validation was performed by comparing the processed results to images obtained from scanning electron microscopy (SEM). It was observed that super-resolution processing can significantly improve the low-resolution micro-CT image quality without physically reducing the sample size typically required for high-resolution scanning. It is expected that this technique could assist practitioners reveal features absent in small samples (e.g. large fractures and or rock textures). Furthermore, images restored through super-resolution processing maintain the FOV of the lower resolution scan, a task that would be comparatively expensive and time consuming to acquire in a high-resolution scan. The workflow proposed in this study has a significant impact on a range of fields including the numerical prediction of rock permeability, and segmentation for advanced mineral analysis. Graphical abstract: Highlights: Application of neural network algorithms for image quality enhancement. Improvement of image segmentation quality using super-resolution. Application of deep learning techniques in geosciences.