资源环境科技发展态势分析平台

High‐resolution X‐ray microcomputed tomography (micro‐CT) data is required for the accurate determination of rock petrophysical properties. High‐resolution data, however, results in a small field‐of‐view, and thus the representativeness of a simulation domain can be brought into question when dealing with geophysical applications. This paper introduces a cycle‐in‐cycle generative adversarial network (CinCGAN) to improve the resolution of 3D micro‐CT data using unpaired training images. Effective porosity, Euler characteristic, pore size distribution and absolute permeability are measured on super‐resolution and high‐resolution ground‐truth images to evaluate the physical accuracy of the proposed CinCGAN. The results demonstrate that CinCGAN provides physically accurate images with an order of magnitude larger field‐of‐view when compared to typical micro‐CT methods. This unlocks new ways for the geophysical characterisation of subsurface rocks with broad implications for flow modelling in highly heterogeneous rocks or fundamental studies on non‐local forces that extend beyond domain sizes typically used for pore‐scale simulation.