气候变化领域集成服务门户(CCPortal): An Innovative Application of Generative Adversarial Networks for Physically Accurate Rock Images With an Unprecedented Field of View

CCPortal

DOI	10.1029/2020GL089029
	An Innovative Application of Generative Adversarial Networks for Physically Accurate Rock Images With an Unprecedented Field of View
	Niu Y.; Wang Y.D.; Mostaghimi P.; Swietojanski P.; Armstrong R.T.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:23
英文摘要	High-resolution X-ray microcomputed tomography (micro-CT) data are used for the accurate determination of rock petrophysical properties. High-resolution data, however, result 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 applies a cycle-in-cycle generative adversarial network (CinCGAN) to improve the resolution of 3-D micro-CT data and create a super-resolution image 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 pathways for the geophysical characterization of subsurface rocks with broad implications for flow modeling in highly heterogeneous rocks or fundamental studies on nonlocal forces that extend beyond domain sizes typically used for pore-scale simulation. ©2020. American Geophysical Union. All Rights Reserved.
英文关键词	Image enhancement; Optical resolving power; Petrophysics; Pore size; Rocks; Absolute permeability; Euler characteristic; Geophysical applications; Geophysical characterization; High resolution data; Petrophysical properties; Pore-scale simulation; X ray micro-computed tomography; Computerized tomography; computer system; field of view; flow modeling; image resolution; innovation; magnitude; microstructure; permeability; porosity; three-dimensional modeling; tomography
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/169374
作者单位	School of Minerals and Energy Resources Engineering, The University of New South Wales, Sydney, NSW, Australia; School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia
推荐引用方式 GB/T 7714	Niu Y.,Wang Y.D.,Mostaghimi P.,et al. An Innovative Application of Generative Adversarial Networks for Physically Accurate Rock Images With an Unprecedented Field of View[J],2020,47(23).
APA	Niu Y.,Wang Y.D.,Mostaghimi P.,Swietojanski P.,&Armstrong R.T..(2020).An Innovative Application of Generative Adversarial Networks for Physically Accurate Rock Images With an Unprecedented Field of View.Geophysical Research Letters,47(23).
MLA	Niu Y.,et al."An Innovative Application of Generative Adversarial Networks for Physically Accurate Rock Images With an Unprecedented Field of View".Geophysical Research Letters 47.23(2020).