PDF(11111 KB)
Progress on image processing of rock CT
HuangXing ZHU, YuanZhong ZHANG
Prog Geophy ›› 2025, Vol. 40 ›› Issue (5) : 1954-1976.
PDF(11111 KB)
PDF(11111 KB)
Progress on image processing of rock CT
CT images of rocks could reflect the structural characteristics of pores and fractures. Processing of CT images and extracting geological information are crucial for studying the pore structures of rocks and analyzing their physical responses, which would enhance our understanding of rock characteristics aiding in resource exploration and geophysical research. The principles, applicable conditions, application effects, and influencing factors of rock CT image processing methods were all reviewed. The image processing algorithms are categorized into three types, digital image-based processing methods, machine learning-based processing methods and super-resolution image processing methods. The results indicate that: (1) The digital image processing methods are straightforward and easy to implement, with a wide range of applications, however, rely heavily on prior knowledge from human operators; (2) Machine learning-based image processing methods could automatically, objectively, accurately, and quickly extract information from images, nevertheless, there is a shortage of rock CT image data and the generalization ability of models needs to be improved; (3) Super-resolution image processing methods could effectively improve the clarity and details of the image, but their effectiveness in enhancing resolution is limited by the imaging conditions of the rock samples. Integrating artificial intelligence methods offers the potential for the intelligent analysis and automatic recognition of rock CT images, which would accelerate the acquisition and interpretation of geological information.
Rock CT images / Image processing / Deep Learning / Digital rock physics / Artificial intelligence
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|
|
Alizadeh S M, Latham S, Middleton J, et al. 2015. An analysis of sleeve effects for petrophysical measurements using digital core analysis. //Proceedings of International Petroleum Technology Conference. Doha, doi: 10.2523/IPTC-18378-MS.
|
|
|
|
Antle R. 2019. Automated core fracture characterization by computer vision and image analytics of CT images. //Proceedings of SPE Oklahoma City Oil and Gas Symposium. Oklahoma City: SPE, doi: 10.2118/195181-MS.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Chen L C, Papandreou G, Kokkinos I, et al. 2015. Semantic image segmentation with deep convolutional nets and fully connected CRFs. // Proceedings of the 3rd International Conference on Learning Representations. San Diego: ICLR.
|
|
Chen L C, Papandreou G, Kokkinos I, et al. 2017a. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv: 1606.00915, doi: 10.48550/arXiv.1606.00915.
|
|
Chen L C, Papandreou G, Schroff F, et al. 2017b. Rethinking atrous convolution for semantic image segmentation. arXiv: 1706.05587, doi: 10.48550/arXiv.1706.05587.
|
|
Chen L C, Zhu Y K, Papandreou G, et al. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. //Proceedings of the 15th European Conference on Computer Vision. Munich: Springer, 833-851, doi: 10.1007/978-3-030-01234-2_49.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Dong C, Loy C C, He K M, et al. 2014. Learning a deep convolutional network for image super-resolution. //Proceedings of the 13th European Conference on Computer Vision. Zurich: Springer, 184-199, doi: 10.1007/978-3-319-10593-2_13.
|
|
Dong C, Loy C C, Tang X O. 2016. Accelerating the super-resolution convolutional neural network. //Proceedings of the 14th European Conference on Computer Vision. Amsterdam: Springer, 391-407, doi: 10.1007/978-3-319-46475-6_25.
|
|
|
|
|
|
Gao W S, Zhang X G, Yang L, et al. 2010. An improved Sobel edge detection. //Proceedings of the 2010 3rd International Conference on Computer Science and Information Technology. Chengdu: IEEE, 67-71, doi: 10.1109/ICCSIT.2010.5563693.
|
|
|
|
|
|
|
|
|
|
Jiang F. 2018. Research on segmentation of sandstone thin section images based on semantic identification [Ph. D. thesis](in Chinese). Nanjing: Nanjing University.
|
|
|
|
|
|
|
|
|
|
|
|
Kim J, Lee J K, Lee K M. 2016. Accurate image super-resolution using very deep convolutional networks. //Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 1646-1654.
|
|
Ledig C, Theis L, Huszár F, et al. 2017. Photo-realistic single image super-resolution using a generative adversarial network. //Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 105-114.
|
|
|
|
|
|
|
|
|
|
Li E S, Zhu S L, Zhu B S, et al. 2009. An adaptive edge-detection method based on the canny operator. //Proceedings of 2009 International Conference on Environmental Science and Information Application Technology. Wuhan: IEEE, 465-469, doi: 10.1109/ESIAT.2009.49.
|
|
|
|
|
|
|
|
|
|
Long J, Shelhamer E, Darrell T. 2015. Fully convolutional networks for semantic segmentation. //Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 3431-3440, doi: 10.1109/CVPR.2015.7298965.
|
|
|
|
|
|
|
|
Milletari F, Navab N, Ahmadi S A. 2016. V-Net: fully convolutional neural networks for volumetric medical image segmentation. //Proceedings of the 2016 Fourth International Conference on 3D Vision. Stanford: IEEE, doi: 10.1109/3DV.2016.79.
|
|
|
|
|
|
Ronneberger O, Fischer P, Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation. //Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer, 234-241, doi: 10.1007/978-3-319-24574-4_28.
|
|
Rosenfeld A. 1981. The max Roberts operator is a hueckel-type edge detector. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-3(1): 101-103, doi: 10.1109/TPAMI.1981.4767056.
|
|
|
|
|
|
|
|
|
|
Sheikh Y A, Khan E A, Kanade T. 2007. Mode-seeking by Medoidshifts. //Proceedings of the 2007 IEEE 11th International Conference on Computer Vision. Rio de Janeiro: IEEE, 1-8, doi: 10.1109/ICCV.2007.4408978.
|
|
Shi W Z, Caballero J, Huszár F, et al. 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. //Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 1874-1883.
|
|
|
|
Tai Y, Yang J, Liu X M. 2017. Image super-resolution via deep recursive residual network. //Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2790-2798.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Wang X J, Cao D P, Fu F Q. 2022. Canny edge detection for digital cores based on fractional order differentials. //Proceedings of the 2023 Joint Academic Annual Meeting of Earth Sciences in China(in Chinese). Chinese Geophysical Society, 3, doi: 10.26914/c.cnkihy.2022.083817.
|
|
Wang X T, Yu K, Wu S X, et al. 2018. ESRGAN: enhanced super-resolution generative adversarial networks. //Proceedings of European Conference on Computer Vision. Munich: Springer, doi: 10.1007/978-3-030-11021-5_5.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Yan W C, Sun J M, Xing H L, et al. 2023. The progress of constructing multi-scale three-dimensional digital core models based on deep learning. //Proceedings of 2023 Joint Academic Annual Meeting of Earth Sciences in China(in Chinese). Zhuhai: Chinese Geophysical Society, 3, doi: 10.26914/c.cnkihy.2023.118554.
|
|
|
|
|
|
Yang L, Wu X Y, Zhao D W, et al. 2011. An improved Prewitt algorithm for edge detection based on noised image. //Proceedings of the 2011 4th International Congress on Image and Signal Processing. Shanghai: IEEE, 1197-1200, doi: 10.1109/CISP.2011.6100495.
|
|
|
|
Yu H Y, Zhang Y H, Lebedev M, et al. 2018. Reactive flow in unconsolidated sandstone: application to carbon geosequestration. //Proceedings of Abu Dhabi International Petroleum Exhibition & Conference. Abu Dhabi: SPE, doi: 10.2118/193141-MS.
|
|
|
|
Zhang M L. 2023. Research on rock fracture segmentation in outcrop area based on improved Deeplabv3+ [Master's thesis](in Chinese). Daqing: Northeast Petroleum University.
|
|
Zhang Y J. 2006. An overview of image and video segmentation in the last 40 years. //Zhang Y J ed. Advances in Image and Video Segmentation. Hershey: IRM Press, 1-16.
|
|
|
|
|
|
Zhao H S, Shi J P, Qi X J, et al. 2017. Pyramid scene parsing network. //Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 6230-6239, doi: 10.1109/CVPR.2017.660.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
姜枫. 2018. 基于语义识别的砂岩薄片图像分割方法研究[博士论文]. 南京: 南京大学.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
王新杰, 曹丹平, 付飞琪. 2022. 基于分数阶微分的数字岩心Canny边缘检测. //2022年中国地球科学联合学术年会论文集——专题二十九: 油藏地球物理、专题三十: 油气地球物理. 中国地球物理学会, 3, doi: 10.26914/c.cnkihy.2022.083817.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
闫伟超, 孙建孟, 邢会林, 等. 2023. 基于深度学习的多尺度三维数字岩心模型构建进展. //2023年中国地球科学联合学术年会论文集——专题一百零六数字岩石物理理论及应用、专题一百零七地球生物学与天体生物学. 珠海: 中国地球物理学会, 3, doi: 10.26914/c.cnkihy.2023.118554.
|
|
|
|
|
|
|
|
|
|
张梦柳. 2023. 基于改进Deeplabv3+网络的野外露头区岩石裂缝分割研究[硕士论文]. 大庆: 东北石油大学.
|
|
|
|
|
|
|
|
|
|
|
本文研究工作得到中国石油大学(北京)地球探测与信息技术北京市重点实验室的支持,在此表示感谢.
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