3D Carbonate Digital Rock Reconstruction Using Progressive Growing GAN

The development of digital rock physics relies on the availability of high‐quality 3D digital rock images, which can be directly obtained with X‐ray micro‐Computed Tomography ( μ CT). However, X‐ray μ CT is hampered by its high expenses, small sample size (several millimeters in diameter) and low resolution (in micron scale). Although Scanning Electron Microscope (SEM) provides higher resolution on larger rock samples, it only images the 2D rock surface structure. Thus, 3D digital rock reconstruction from 2D cross‐section images becomes promising in saving imaging cost for μ CT scan and improving image quality by enabling the incorporation of SEM images in 3D digital rock reconstruction. Here, we propose a machine learning method to reconstruct 3D digital rocks from 2D cross‐section images taken at large constant intervals along the axial direction of the rock sample. The key idea is to train a Progressive Growing Generative Adversarial Network (PG‐GAN) to generate high‐quality gray‐scale cross‐section images, and then reconstruct the 3D digital rock by linearly interpolating the inverted latent vectors corresponding to the sparsely scanned images. We apply our method to reconstructing a large‐size high‐resolution 3D image of an Estaillades carbonate rock sample. We demonstrate that both the reconstructed image and the extracted pore network are visually indistinguishable from the ground truth. Overall, our method achieves nine times speedup of the imaging process, and greater than 4,500 times compression of the image data for the Estaillades carbonate rock sample. The PG‐GAN can enlarge the digital rock repository and enable efficient imaging editing in its linear latent space.