Progress on image processing of rock CT

HuangXing ZHU, YuanZhong ZHANG

Prog Geophy ›› 2025, Vol. 40 ›› Issue (5) : 1954-1976.

PDF(11111 KB)
Home Journals Progress in Geophysics
Progress in Geophysics

Abbreviation (ISO4): Prog Geophy      Editor in chief:

About  /  Aim & scope  /  Editorial board  /  Indexed  /  Contact  / 
PDF(11111 KB)
Prog Geophy ›› 2025, Vol. 40 ›› Issue (5) : 1954-1976. DOI: 10.6038/pg2025II0288

Progress on image processing of rock CT

Author information +
History +

Abstract

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.

Key words

Rock CT images / Image processing / Deep Learning / Digital rock physics / Artificial intelligence

Cite this article

Download Citations
HuangXing ZHU , YuanZhong ZHANG. Progress on image processing of rock CT[J]. Progress in Geophysics. 2025, 40(5): 1954-1976 https://doi.org/10.6038/pg2025II0288

References

Achanta R , Shaji A , Smith K , et al. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274- 2282.
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.
Andrä H , Combaret N , Dvorkin J , et al. Digital rock physics benchmarks—Part Ⅰ: imaging and segmentation. Computers & Geosciences, 2013, 50: 25- 32.
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.
Bai Y , Tan M J , Xiao C W , et al. Dynamic classification committee machine-based fluid typing method from wireline logs for tight sandstone gas reservoir. Chinese Journal of Geophysics, 2021, 64(5): 1745- 1758.
Bi F Y , Xiao Z S , Zhang X Z , et al. Automated identification method of shale pore types based on deep learning. Well Logging Technology, 2022, 46(4): 439- 445.
Bizhani M , Ardakani O H , Little E . Reconstructing high fidelity digital rock images using deep convolutional neural networks. Scientific Reports, 2022, 12(1): 4264
Blunt M J , Bijeljic B , Dong H , et al. Pore-scale imaging and modelling. Advances in Water Resources, 2013, 51: 197- 216.
Cai Y H , Teng Q Z , Tu B Y . Automatic extraction of pores in thin slice images of rock castings based on deep learning. Science Technology and Engineering, 2020, 20(28): 11685- 11692.
Chen G J , Jiang Z , Yin C , et al. Rock pore segmentation method based on deep learning. Computer and Digital Engineering, 2023, 51(5): 1157- 1162.
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.
Chen R , Yang S J , Zhu Z H , et al. Research on image recognition of blasting block based on double threshold. Engineering Blasting, 2020, 26(2): 57- 64.
Cheng G J , Zhang F L . Super-resolution reconstruction of rock slice image based on SinGAN. Journal of Xi'an Shiyou University(Natural Science Edition), 2021, 36(2): 116- 121.
Cheng M , Cao J X , You J C , et al. Automatic horizon tracking method based on image semantic segmentation. Progress in Geophysics, 2021, 36(4): 1504- 1511.
Cheng Y Z . Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790- 799.
Chi P , Sun J M , Luo X , et al. Reconstruction of 3D digital rocks with controllable porosity using CVAE-GAN. Geoenergy Science and Engineering, 2023, 230: 212264
Chi P , Sun J M , Yan W C , et al. Multiscale fusion of tight sandstone digital rocks using attention-guided generative adversarial network. Marine and Petroleum Geology, 2024, 160: 106647
Chung T , Wang Y D , Armstrong R T , et al. Voxel agglomeration for accelerated estimation of permeability from micro-CT images. Journal of Petroleum Science and Engineering, 2020, 184: 106577
Cui L K , Sun J M , Yan W C , et al. Construction of multi-scale and-component digital cores based on fusion of different resolution core images. Journal of Jilin University(Earth Science Edition), 2017, 47(6): 1904- 1912.
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.
Fandrich R , Gu Y , Burrows D , et al. Modern SEM-based mineral liberation analysis. International Journal of Mineral Processing, 2007, 84(1-4): 310- 320.
Fukunaga K , Hostetler L . The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 1975, 21(1): 32- 40.
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.
Garcia-Garcia A , Orts-Escolano S , Oprea S , et al. A survey on deep learning techniques for image and video semantic segmentation. Applied Soft Computing, 2018, 70: 41- 65.
Geng C , Yang Y F , Gao Y . Optimization of image processing method based on rock CT images of different resolutions. Science Technology and Engineering, 2014, 14(2): 1- 4.
Gerchberg R W . Super-resolution through Error Energy Reduction. Optica Acta: International Journal of Optics, 1974, 21(9): 709- 720.
Iassonov P , Gebrenegus T , Tuller M . Segmentation of X-ray computed tomography images of porous materials: a crucial step for characterization and quantitative analysis of pore structures. Water Resources Research, 2009, 45(9):
Jiang F. 2018. Research on segmentation of sandstone thin section images based on semantic identification [Ph. D. thesis](in Chinese). Nanjing: Nanjing University.
Ju Y , Huang Y H , Zheng J T , et al. Multi-thread parallel algorithm for reconstructing 3D large-scale porous structures. Computers & Geosciences, 2017, 101: 10- 20.
Kamrava S , Tahmasebi P , Sahimi M . Enhancing images of shale formations by a hybrid stochastic and deep learning algorithm. Neural Networks, 2019, 118: 310- 320.
Kapur J N , Sahoo P K , Wong A K C . A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, 1985, 29(3): 273- 285.
Karimpouli S , Tahmasebi P . Segmentation of digital rock images using deep convolutional autoencoder networks. Computers & Geosciences, 2019, 126: 142- 150.
Khan J F , Bhuiyan S M A , Adhami R R . Image segmentation and shape analysis for road-sign detection. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1): 83- 96.
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.
Levinshtein A , Stere A , Kutulakos K N , et al. TurboPixels: fast superpixels using geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2290- 2297.
Li B K , Nie X , Cai J C , et al. U-Net model for multi-component digital rock modeling of shales based on CT and QEMSCAN images. Journal of Petroleum Science and Engineering, 2022, 216: 110734
Li C L , Yan W L , Wu H L , et al. Calculation of oil saturation in clay-rich shale reservoirs: a case study of Qing 1 member of Cretaceous Qingshankou Formation in Gulong Sag, Songliao Basin, NE China. Petroleum Exploration and Development, 2022, 49(6): 1168- 1178.
Li C N , Li C F , Liu C L , et al. A method to calculate the porosity of Berea sandstone based on CT digital images. Nuclear Electronics & Detection Technology, 2017, 37(5): 482- 487.
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.
Lin C Y , Wu Y Q , Ren L H , et al. Review of digital core modeling methods. Progress in Geophysics, 2018, 33(2): 679- 689.
Liu H , Ren Y L , Li X , et al. Connotation and prospect of intelligent recognition technology for cores. Acta Petrolei Sinica, 2024, 45(8): 1296- 1308.
Liu M L , Mukerji T . Multiscale fusion of digital rock images based on deep generative adversarial networks. Geophysical Research Letters, 2022, 49(9):
Liu W B , Wang Z D , Liu X H , et al. A survey of deep neural network architectures and their applications. Neurocomputing, 2017, 234: 11- 26.
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.
Lu F M , Meng R G , Zhang J H , et al. Research of complex fault recognition method based on UNet+ + network and transfer learning technique. Progress in Geophysics, 2022, 37(3): 1100- 1111.
Ma Z Q , Zuo Y L , Lu C H . Application of computed tomography technology(CT)in meso-structure observation of mineral rocks. Industrial Minerals & Processing, 2019, 48(3): 4- 8.
McClure J E , Prins J F , Miller C T . A novel heterogeneous algorithm to simulate multiphase flow in porous media on multicore CPU-GPU systems. Computer Physics Communications, 2014, 185(7): 1865- 1874.
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.
Mostaghimi P , Blunt M J , Bijeljic B . Computations of absolute permeability on micro-CT images. Mathematical Geosciences, 2013, 45(1): 103- 125.
Mostaghimi P , Liu M , Arns C H . Numerical simulation of reactive transport on micro-CT images. Mathematical Geosciences, 2016, 48(8): 963- 983.
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.
Saxena N , Day-Stirrat R J , Hows A , et al. Application of deep learning for semantic segmentation of sandstone thin sections. Computers & Geosciences, 2021, 152: 104778
Schlüter S , Sheppard A , Brown K , et al. Image processing of multiphase images obtained via X-ray microtomography: a review. Water Resources Research, 2014, 50(4): 3615- 3639.
Sezgin M , Sankur B . Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 2004, 13(1): 146- 165.
Shan L Q , Liu C Q , Liu Y C , et al. Rock CT image super-resolution using residual dual-channel attention generative adversarial network. Energies, 2022, 15(14): 5115
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.
Sun J M , Sun X J , Chi P , et al. Digital cores and digital wellbore technology: application and research progress. Geophysical Prospecting for Petroleum, 2023, 65(2): 806- 819.
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.
Tan M J . Digital rock physics and its progress in log interpretation. Well Logging Technology, 2022, 46(4): 371- 379.
Tian S S , Bowen L , Liu B , et al. A method for automatic shale porosity quantification using an Edge-Threshold Automatic Processing(ETAP)technique. Fuel, 2021, 304: 121319
Torbati-Sarraf H , Niverty S , Singh R , et al. Machine-learning-based algorithms for automated image segmentation techniques of transmission X-ray microscopy(TXM): microstructure characterization: descriptors, data-intensive techniques, and uncertainty quantification. JOM, 2021, 73(7): 2173- 2184.
Tremeau A , Borel N . A region growing and merging algorithm to color segmentation. Pattern Recognition, 1997, 30(7): 1191- 1203.
Ulupinar F , Medioni G . Refining edges detected by a LoG operator. Computer Vision, Graphics, and Image Processing, 1990, 51(3): 275- 298.
Varfolomeev I , Yakimchuk I , Safonov I . An application of deep neural networks for segmentation of microtomographic images of rock samples. Computers, 2019, 8(4): 72
Vincent L , Soille P . Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(6): 583- 598.
Wang H T , Yang Y , Geng Z B . Investigation the effect of the greyscale threshold on the petrophysical properties of the reconstructed core model after the image segmentation. Progress in Geophysics, 2018, 33(6): 2456- 2461.
Wang K , Fu Q , Xu C , et al. Threshold segmentation method of CT scanning data of coal and rock samples considering beam hardening effect and its application. Coal Geology & Exploration, 2023, 51(4): 11- 22.
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.
Wang Y D , Armstrong R T , Mostaghimi P . Enhancing resolution of digital rock images with super resolution convolutional neural networks. Journal of Petroleum Science and Engineering, 2019, 182: 106261
Wang Y D , Armstrong R T , Mostaghimi P . Boosting resolution and recovering texture of 2D and 3D micro-CT images with deep learning. Water Resources Research, 2020a, 56(1): e2019WR026052
Wang Y D , Chung T , Armstrong R T , et al. Accelerated computation of relative permeability by coupled morphological and direct multiphase flow simulation. Journal of Computational Physics, 2020b, 401: 108966
Wu X , Xiao Z S , Zhang Y H , et al. Progress and prospect of multiscale digital rock modeling. Journal of Jilin University(Earth Science Edition), 2024, 54(5): 1736- 1751.
Xiao F , Li G L , Chen Y L , et al. Exploration of digital rock construction method and application prospect. Well Logging Technology, 2021, 45(3): 240- 245.
Xing Z H , Yao J , Liu L , et al. Digital rock resolution enhancement and detail recovery with multi attention neural network. Geoenergy Science and Engineering, 2023, 227: 211920
Xiong F , Liao Y L , Cao W T , et al. Study on automatic recognition of rock lithology based on convolutional neural network and deep transfer learning. Safety and Environmental Engineering, 2023, 30(4): 26- 34.
Xu T N , Zhou H L , Liu X Y , et al. Seismic facies identification based on Res-Unet and transfer learning. Progress in Geophysics, 2024, 39(1): 319- 333.
Xu Y J , Teng Q Z , Wu X H , et al. Semi-segmentation of rock CT images using the correlation of adjacent frames. Journal of Image and Graphics, 2015, 20(10): 1340- 1345.
Xue D J , Tang Q C , Wang A , et al. FCN-based intelligent identification and fractal reconstruction of pore-fracture network in coal by micro CT scanning. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(6): 1203- 1221.
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 B , Liu Y Y , Pan M . Rock-type classification based on Minkowski functionals and K-means cluster analysis. Science Technology and Engineering, 2017, 17(22): 63- 67.
Yang J , Ding R W , Lin N T , et al. Research progress of intelligent identification of seismic faults based on deep learning. Progress in Geophysics, 2022, 37(1): 298- 311.
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.
Yang X , Li X F , Tang Y B , et al. Study on Non-Newtonian fluid displacement patterns based on the pore network model. Chinese Journal of Geophysics, 2023, 66(12): 5157- 5172.
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 J F , Zhang X J , Yang G S , et al. A method of rock CT image segmentation and quantification based on clustering algorithm. Journal of Xi'an University of Science and Technology, 2016, 36(2): 171- 175.
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.
Zhang Y L , Xing H L , Li S Z , et al. Fracture extraction and repair of 2D rock image based on hybrid algorithm of ant colony and Canny edge detection operator. Geotectonica et Metallogenia, 2021, 45(1): 242- 251.
Zhang Z H , Lu R Q , Liao X L , et al. Inversion of magnetic anomaly and magnetic gradient anomaly based on fully convolution network. Progress in Geophysics, 2021, 36(1): 325- 337.
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.
Zhao J P , Chen H , Li N , et al. Research advance of petrophysical application based on digital core technology. Progress in Geophysics, 2020, 35(3): 1099- 1108.
Zhao J Y , Cai J C . Generalization ability analysis of digital rock image segmentation based on Unet+ + network. Journal of China University of Petroleum(Edition of Natural Science), 2024, 48(2): 118- 125.
Zhu L X , Zheng Y . Applications of self-attention SRGAN in super resolution reconstruction of rock CT image. Journal of Xi'an Shiyou University(Natural Science Edition), 2022, 37(2): 131- 137.
, 茂金 , 承文 , 等. 致密砂岩气藏动态分类委员会机器测井流体识别方法. 地球物理学报, 2021, 64(5): 1745- 1758.
飞宇 , 占山 , 学忠 , 等. 基于深度学习的页岩孔隙类型自动识别方法. 测井技术, 2022, 46(4): 439- 445.
宇恒 , 奇志 , 秉宇 . 基于深度学习的岩石铸体薄片图像孔隙自动提取. 科学技术与工程, 2020, 20(28): 11685- 11692.
国军 , , , 等. 基于深度学习的岩石孔隙分割方法. 计算机与数字工程, 2023, 51(5): 1157- 1162.
, 仕教 , 忠华 , 等. 基于双门限阈值的爆破块度图像识别研究. 工程爆破, 2020, 26(2): 57- 64.
国建 , 福临 . 基于SinGAN的岩石薄片图像超分辨率重建. 西安石油大学学报(自然科学版), 2021, 36(2): 116- 121.
, 俊兴 , 加春 , 等. 基于图像语义分割的层位自动追踪方法. 地球物理学进展, 2021, 36(4): 1504- 1511.
利凯 , 建孟 , 伟超 , 等. 基于多分辨率图像融合的多尺度多组分数字岩心构建. 吉林大学学报(地球科学版), 2017, 47(6): 1904- 1912.
, 永飞 , . 不同分辨率岩石CT图像的优化处理方法. 科学技术与工程, 2014, 14(2): 1- 4.
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.
潮流 , 伟林 , 宏亮 , 等. 富黏土页岩储集层含油饱和度计算方法——以松辽盆地古龙凹陷白垩系青山口组一段为例. 石油勘探与开发, 2022, 49(6): 1168- 1178.
晨安 , 承峰 , 昌岭 , 等. CT图像法计算Berea砂岩孔隙度. 核电子学与探测技术, 2017, 37(5): 482- 487.
承焰 , 玉其 , 丽华 , 等. 数字岩心建模方法研究现状及展望. 地球物理学进展, 2018, 33(2): 679- 689.
, 义丽 , , 等. 岩心智能识别技术内涵与展望. 石油学报, 2024, 45(8): 1296- 1308.
凤明 , 瑞刚 , 军华 , 等. UNet+ +和迁移学习相结合的复杂断裂识别方法研究. 地球物理学进展, 2022, 37(3): 1100- 1111.
志强 , 艳丽 , 春华 . 计算机断层扫描(CT)技术在矿物岩石细观结构观测中的应用. 化工矿物与加工, 2019, 48(3): 4- 8.
建孟 , 晓娟 , , 等. 数字岩心和数字井筒技术研究与应用进展. 石油物探, 2023, 62(5): 806- 819.
茂金 . 数字岩石物理学及测井解释应用概论. 测井技术, 2022, 46(4): 371- 379.
海涛 , , 尊博 . 图像分割中灰度阈值对岩石物理属性影响的研究. 地球物理学进展, 2018, 33(6): 2456- 2461.
, , , 等. 考虑射束硬化的煤岩CT数据阈值分割方法及应用. 煤田地质与勘探, 2023, 51(4): 11- 22.
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.
, 占山 , 永浩 , 等. 多尺度数字岩石建模进展与展望. 吉林大学学报(地球科学版), 2024, 54(5): 1736- 1751.
, 戈理 , 玉林 , 等. 数字岩石构建方法及应用前景. 测井技术, 2021, 45(3): 240- 245.
, 一凡 , 伟腾 , 等. 基于卷积神经网络-深度迁移学习的岩性自动识别研究. 安全与环境工程, 2023, 30(4): 26- 34.
天恩 , 怀来 , 兴业 , 等. 基于Res-Unet与迁移学习的地震相识别. 地球物理学进展, 2024, 39(1): 319- 333.
永进 , 奇志 , 晓红 , 等. 利用层间相关性的岩心CT图像半自动分割方法. 中国图象图形学报, 2015, 20(10): 1340- 1345.
东杰 , 麒淳 , , 等. 煤岩微观相态FCN智能识别与分形重构. 岩石力学与工程学报, 2020, 39(6): 1203- 1221.
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.
, 钰洋 , . 基于Minkowski泛函和K-means聚类算法的岩石类型划分. 科学技术与工程, 2017, 17(22): 63- 67.
, 仁伟 , 年添 , 等. 基于深度学习的地震断层智能识别研究进展. 地球物理学进展, 2022, 37(1): 298- 311.
, 星甫 , 雁冰 , 等. 基于孔隙网络模型的非牛顿流体驱替模式研究. 地球物理学报, 2023, 66(12): 5157- 5172.
嘉凡 , 雪娇 , 更社 , 等. 基于聚类算法的岩石CT图像分割及量化方法. 西安科技大学学报, 2016, 36(2): 171- 175.
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.
愉玲 , 会林 , 三忠 , 等. 基于蚁群和Canny边缘检测算子混合算法的二维岩石图像裂隙特征提取与修复研究. 大地构造与成矿学, 2021, 45(1): 242- 251.
志厚 , 润琪 , 晓龙 , 等. 基于全卷积神经网络的磁异常及磁梯度异常反演. 地球物理学进展, 2021, 36(1): 325- 337.
建鹏 , , , 等. 三维数字岩心技术岩石物理应用研究进展. 地球物理学进展, 2020, 35(3): 1099- 1108.
久玉 , 建超 . 基于Unet+ +网络的数字岩心图像分割泛化能力. 中国石油大学学报(自然科学版), 2024, 48(2): 118- 125.
联祥 , . 自注意力SRGAN在岩石CT图像超分辨中的应用研究. 西安石油大学学报(自然科学版), 2022, 37(2): 131- 137.

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.

RIGHTS & PERMISSIONS

Copyright ©2025 Progress in Geophysics. All rights reserved.
PDF(11111 KB)

Accesses

Citation

Detail

Sections
Recommended

/