Two images comparing a high-resolution pore network in rock and a reconstruction of the same by the machine learning model.
The high-resolution pore network (left) is accurately reconstructed by the machine learning model (right). In addition to the visual similarity, total porosity also matches closely: 11.08% (left) versus 11.2% (right). Credit: You et al. [2021], Figure 7
Source: Journal of Geophysical Research: Solid Earth

Digital rock physics utilizes a paradigm of first taking a digital image of a rock and then performing a computer simulation using the digital image. 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. Performing interpolation of the rock images directly results in a low-quality reconstruction, but the PG-GAN produces a high-quality result after interpolation in the low-dimensional space. Using the PG-GAN to interpolate in the low-dimensional space enables the accurate digital reconstruction of a rock using fewer 2D slices, which reduces the cost of the process.

Citation: You, N., Li, Y. E., & Cheng, A. [2021]. 3D carbonate digital rock reconstruction using progressive growing GAN. Journal of Geophysical Research: Solid Earth, 126, e2021JB021687.

This research article is part of a cross-journal special collection on “Machine Learning for Solid Earth Observation, Modeling, and Understanding”. Find out more and read other articles.

—Daniel O’Malley, Associate Editor, JGR: Solid Earth

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