Reconstructing Rocks with Machine Learning

This has many applications, such as hydrogeology and geologic carbon dioxide sequestration. The imaging portion of the task can be costly because high-resolution images of 3D rocks often must be pieced-together by taking many images of 2D rock slices. You et al. [2021] utilize a machine learning technique called a "progressive growing generative adversarial network" (or PG-GAN) to reduce the cost of producing high-resolution 3D rock images. The PG-GAN learns to generate realistic, high-dimensional rock images from noise in a low-dimensional space. A given rock image can be reconstructed by finding an optimal point in the low-dimensional space.