A generative adversarial network (GAN) model is applied to the outpainting reconstruction on the micro grain and pore structure of sandstone thin-section image, and the semantics of the predicted image is analyzed in the model. The findings demonstrate that the model can predict a lager view of sandstone microstructure, which is 2.25 times the size of the original vision, and has good performance for different types of rock image semantics. The predicted image semantics, such as surface texture of different grains, grain morphology and complex contact relationship between multiple grains are consistent with the real results. However, in the task of outpainting reconstruction on microscopic special phenomenon in rock thin-section, the model lacks sensitivity to special phenomenon. In the task of reconstruction on pore structure, the prediction error of micropore distribution is larger than that of common pore spaces such as intergranular pores, fractures and dissolution pores. The prediction performance of reconstruction results of different pore spaces may be related to pore features (such as pore size and connectivity).

利用生成对抗网络(GAN)模型, 对砂岩薄片图像的微观颗粒和孔隙结构进行视野外重建, 并对预测图像的语义进行评价。结果表明, 模型能够预测2.25倍于原始视野的砂岩微观结构, 并且针对不同类型的岩石图像语义均具有良好的性能。模型对不同颗粒的表面纹理、颗粒形态以及多颗粒间复杂接触关系等语义的图像视野外预测结果与真实图像较为吻合。但是, 在微观特殊现象图像的视野外重建任务中, 模型缺乏对特殊现象的敏感性。在孔隙结构重建时, 模型对微孔面孔率的预测误差大于粒间孔、裂隙和溶蚀孔等孔隙空间。不同孔隙空间重建图像的预测效果可能与孔隙特征(如孔径大小和连通性)有关。