A hybrid of statistical and conditional generative adversarial neural network approaches for reconstruction of 3D porous media (ST-CGAN)

Abstract A coupled statistical and conditional generative adversarial neural network is used for 3D reconstruction of both homogeneous and heterogeneous porous media from a single two-dimensional image. A statistical approach feeds the deep network with conditional data, and then the reconstruction is trained on a deep generative network. The conditional nature of the generative model helps in network stability and convergence which has been optimized through a gradient-descent-based optimization method. Moreover, this coupled approach allows the reconstruction of heterogeneous samples, a critical and serious challenge in conventional reconstruction methods. The main contribution of this work is to develop an adaptable framework that can efficiently reconstitute heterogeneous porous media using the power of conditional generative adversarial networks. The reconstruction time is accelerated approximately 1000-fold compared to traditional statistical reconstruction methods. Various matching criteria in both morphological and physical characteristics are used to evaluate the model performance. To validate the approach, the reconstructed realizations have been compared to the models generated by a conventional 3D GAN along with a well-known statistical method. The results confirm that the proposed approach is a reliable framework for extracting information from a single 2D image to reconstruct 3D microstructures.