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 (μCT). However, X‐ray μCT 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 μCT 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.

中文翻译：

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使用渐进式GAN的3D碳酸盐数字岩重建

数字岩石物理学的发展依赖于高质量3D数字岩石图像的可用性，这些图像可以通过X射线微计算机断层扫描（μCT）直接获得。但是，X射线μCT受其高昂的费用，小样本量（直径几毫米）和低分辨率（微米级）的困扰。尽管扫描电子显微镜（SEM）在较大的岩石样本上提供了更高的分辨率，但它仅对二维岩石表面结构成像。因此，从2D断面图像重建3D数字岩石在节省μ的成像成本方面很有前途通过将SEM图像合并到3D数字岩石重建中，CT扫描和改善图像质量。在此，我们提出了一种机器学习方法，用于从沿岩石样本轴向方向以较大的恒定间隔拍摄的2D横截面图像中重建3D数字岩石。关键思想是训练渐进式生成对抗网络（PG-GAN）以生成高质量的灰度横截面图像，然后通过线性插值与稀疏扫描图像相对应的反潜矢量来重建3D数字岩石。我们将我们的方法应用于重建Estaillades碳酸盐岩样品的大尺寸高分辨率3D图像。我们证明重建的图像和提取的孔隙网络在视觉上都与地面真实情况没有区别。全面的，我们的方法使成像过程的速度提高了9倍，对Estaillades碳酸盐岩样品的图像数据进行了大于4,500倍的压缩。PG‐GAN可以扩展数字岩石存储库，并在其线性潜在空间中实现有效的图像编辑。