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Advances in Geo-Energy Research 2022, 6(2): 157-168 https://doi.org/10.46690/ager.2022.02.07
Original Article | Open Access | Issue | Published: 25 February 2022

Super-resolution reconstruction of digital rock CT images based on residual attention mechanism

Show Author's Information Liqun Shan1,2,  Xueyuan Bai1,  Chengqian Liu1,  Yin Feng2,  Yanchang Liu1 ( ),  Yanyan Qi3
School of physics and electronic engineering, Northeast Petroleum University, Daqing 163318, P. R. China
Petroleum engineering department, University of Louisiana at Lafayette, Lafayette 70504, USA
Xiangyang Branch of China Telecom Co. LTD, Xiangyang 441011, P. R. China
Keywords:
super resolution, reconstruction, residual network, Digital rock, attentional mechanism
Cite this article:
Shan L, Bai X, Liu C, et al. Super-resolution reconstruction of digital rock CT images based on residual attention mechanism. Advances in Geo-Energy Research, 2022, 6(2): 157-168. https://doi.org/10.46690/ager.2022.02.07
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Abstract Full text About this article

Computer tomography technology is widely used in geological exploration because it is a nondestructive and three-dimensional imaging method that can be integrated with computer simulation. However, the large-scale application of the computer tomography technique is limited by economic costs and time consumption. Therefore, it is challenging and intractable to indicate the pore structure characteristics of rock. To address this issue, a super-resolution reconstruction algorithm based on convolutional neural networks, residual learning, and attention mechanism was proposed to generate super-resolution images in this study. This algorithm was applied to the reconstruction of carbonate rock and sandstone. The performance of two-dimensional image reconstruction was evaluated by quantitative extraction and qualitative visualization. The results from experiments indicate that the built model performs well on different upscaling factors and is superior to the existing super-resolution approaches based on convolutional neural network.


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About this article

Super-resolution reconstruction of digital rock CT images based on residual attention mechanism

Show Author's information Liqun Shan1,2, Xueyuan Bai1, Chengqian Liu1, Yin Feng2, Yanchang Liu1 ( ), Yanyan Qi3
School of physics and electronic engineering, Northeast Petroleum University, Daqing 163318, P. R. China
Petroleum engineering department, University of Louisiana at Lafayette, Lafayette 70504, USA
Xiangyang Branch of China Telecom Co. LTD, Xiangyang 441011, P. R. China

Abstract

Computer tomography technology is widely used in geological exploration because it is a nondestructive and three-dimensional imaging method that can be integrated with computer simulation. However, the large-scale application of the computer tomography technique is limited by economic costs and time consumption. Therefore, it is challenging and intractable to indicate the pore structure characteristics of rock. To address this issue, a super-resolution reconstruction algorithm based on convolutional neural networks, residual learning, and attention mechanism was proposed to generate super-resolution images in this study. This algorithm was applied to the reconstruction of carbonate rock and sandstone. The performance of two-dimensional image reconstruction was evaluated by quantitative extraction and qualitative visualization. The results from experiments indicate that the built model performs well on different upscaling factors and is superior to the existing super-resolution approaches based on convolutional neural network.

Keywords: super resolution, reconstruction, residual network, Digital rock, attentional mechanism

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Publication history

Received: 29 January 2022
Revised: 20 February 2022
Accepted: 22 February 2022
Published: 25 February 2022
Issue date: April 2022

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© The Author(s) 2022.

Acknowledgements

This work was supported by Fund Projects: Natural Science Foundation of Hebei Province (E2021107005), Northeast Petroleum University Cultivation Fund (2018GP2D-04).

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Open Access This article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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