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Journal articles on the topic 'Pore segmentation'

Author: Grafiati

Published: 7 September 2024

Last updated: 31 July 2025

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1

Liu, Lei, Qiaoling Han, Yue Zhao, and Yandong Zhao. "A Novel Method Combining U-Net with LSTM for Three-Dimensional Soil Pore Segmentation Based on Computed Tomography Images." Applied Sciences 14, no. 8 (2024): 3352. http://dx.doi.org/10.3390/app14083352.

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The non-destructive study of soil micromorphology via computed tomography (CT) imaging has yielded significant insights into the three-dimensional configuration of soil pores. Precise pore analysis is contingent on the accurate transformation of CT images into binary image representations. Notably, segmentation of 2D CT images frequently harbors inaccuracies. This paper introduces a novel three-dimensional pore segmentation method, BDULSTM, which integrates U-Net with convolutional long short-term memory (CLSTM) networks to harness sequence data from CT images and enhance the precision of pore

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Fu, Yinkai, Zihan Huang, Yue Zhao, Benye Xi, Yandong Zhao, and Qiaoling Han. "A weakly supervised soil pore segmentation method based on traditional segmentation algorithm." CATENA 249 (February 2025): 108660. https://doi.org/10.1016/j.catena.2024.108660.

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Silva, Italo Francyles Santos da, Alan de Carvalho Araújo, João Dallyson Sousa de Almeida, Anselmo Cardoso de Paiva, Aristófanes Corrêa Silva, and Deane Roehl. "Soil Structure Analysis with Attention: A Deep Deep-Learning-Based Method for 3D Pore Segmentation and Characterization." AgriEngineering 7, no. 2 (2025): 27. https://doi.org/10.3390/agriengineering7020027.

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The pore structure plays a crucial role in soil systems. It affects a range of processes essential for soil ecological functions, such as the transport and retention of water and nutrients, as well as gas exchanges. The mechanical and hydrological characteristics of soil are predominantly determined by the three-dimensional pore pore-space structure. A precise analysis of pore structure can help specialists understand how these shapes impact plant root activity, leading to better cultivation practices. X-ray computed tomography provides detailed information without destroying the sample. Howev

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Berg, Steffen, Nishank Saxena, Majeed Shaik, and Chaitanya Pradhan. "Generation of ground truth images to validate micro-CT image-processing pipelines." Leading Edge 37, no. 6 (2018): 412–20. http://dx.doi.org/10.1190/tle37060412.1.

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Digital rock technology and pore-scale physics have become increasingly relevant topics in a wide range of porous media with important applications in subsurface engineering. This technology relies heavily on images of pore space and pore-level fluid distribution determined by X-ray microcomputed tomography (micro-CT). Digital images of pore space (or pore-scale fluid distribution) are typically obtained as gray-level images that first need to be processed and segmented to obtain the binary images that uniquely represent rock and pore (including fluid phases). This processing step is not trivi

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Yang, Eomzi, Dong Hun Kang, and Tae Sup Yun. "Reliable estimation of hydraulic permeability from 3D X-ray CT images of porous rock." E3S Web of Conferences 205 (2020): 08004. http://dx.doi.org/10.1051/e3sconf/202020508004.

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The hydraulic permeability is a key parameter for simulating the flow-related phenomenon so that its accurate estimation is crucial in both experimental and numerical simulation studies. 3D pore structure can be readily taken by X-ray computed tomography (CT) and it often serves as a flow domain for pore-scale simulation. However, one encounters the challenges in segmenting the authentic pore structure owing to the finite size of image resolution and segmentation methods. Therefore, the loss of structural information in pore space seems unavoidable to result in the unreliable estimation of per

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Li, Mingjiang, Pan Zhang, and Tao Hai. "Pore extraction method of rock thin section based on Attention U-Net." Journal of Physics: Conference Series 2467, no. 1 (2023): 012016. http://dx.doi.org/10.1088/1742-6596/2467/1/012016.

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Abstract This paper proposes a solution to the shortcomings of traditional segmentation methods. The labeling method uses the incomplete labeling method in weakly supervised labeling to simplify labeling and combines transfer learning to initialize the weight of the network in advance. According to the above ideas, an end-to-end deep learning model is trained. The fine rock particles have a greater segmentation impact, and in addition to that, when compared with the popular deep learning semantic segmentation approaches, they also have a significant improvement. The next phase is to continue i

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Idowu, N. A. A., C. Nardi, H. Long, T. Varslot, and P. E. E. Øren. "Effects of Segmentation and Skeletonization Algorithms on Pore Networks and Predicted Multiphase-Transport Properties of Reservoir-Rock Samples." SPE Reservoir Evaluation & Engineering 17, no. 04 (2014): 473–83. http://dx.doi.org/10.2118/166030-pa.

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Summary Networks of large pores connected by narrower throats (pore networks) are essential inputs into network models that are routinely used to predict transport properties from digital rock images. Extracting pore networks from microcomputed-tomography (micro-CT) images of rocks involves a number of steps: filtering, segmentation, skeletonization, and others. Because of the amount of clay and its distribution, the segmentation of micro-CT images is not trivial, and different algorithms exist for achieving this. Similarly, several methods are available for skeletonizing the segmented images

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Lu, An Qun, Shou Zhi Zhang, and Qian Tian. "Matlab Image Processing Technique and Application in Pore Structure Characterization of Hardened Cement Pastes." Advanced Materials Research 785-786 (September 2013): 1374–79. http://dx.doi.org/10.4028/www.scientific.net/amr.785-786.1374.

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Based on Matlab image processing technique and backscattered electron image analysis method, a characterization method is set up to make quantitative analysis on pore structure of hardened cement pastes. Adopt Matlab to acquire images, and carry out gradation and binarization processing for them; use the combination method of local threshold segmentation and histogram segmentation to obtain pore structure characteristics. The results showed that evolution law of pore structure of fly ash cement pastes via Matlab image analysis method is similar to the conclusion obtained through BET and DVS. S

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Liu, Yifei, and Dong-Sheng Jeng. "Pore Structure of Grain-Size Fractal Granular Material." Materials 12, no. 13 (2019): 2053. http://dx.doi.org/10.3390/ma12132053.

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Numerous studies have proven that natural particle-packed granular materials, such as soil and rock, are consistent with the grain-size fractal rule. The majority of existing studies have regarded these materials as ideal fractal structures, while few have viewed them as particle-packed materials to study the pore structure. In this study, theoretical analysis, the discrete element method, and digital image processing were used to explore the general rules of the pore structures of grain-size fractal granular materials. The relationship between the porosity and grain-size fractal dimension was

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Zel, Ivan, Murat Kenessarin, Sergey Kichanov, Kuanysh Nazarov, Maria Bǎlǎșoiu, and Denis Kozlenko. "Pore Segmentation Techniques for Low-Resolution Data: Application to the Neutron Tomography Data of Cement Materials." Journal of Imaging 8, no. 9 (2022): 242. http://dx.doi.org/10.3390/jimaging8090242.

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The development of neutron imaging facilities provides a growing range of applications in different research fields. The significance of the obtained structural information, among others, depends on the reliability of phase segmentation. We focused on the problem of pore segmentation in low-resolution images and tomography data, taking into consideration possible image corruption in the neutron tomography experiment. Two pore segmentation techniques are proposed. They are the binarization of the enhanced contrast data using the global threshold, and the segmentation using the modified watershe

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Su, Yanchun, Chunhua Zhao, Xianjie Li, et al. "A New Transformation Method of the T2 Spectrum Based on Ordered Clustering—A Case Study on the Pore-Throat Utilization Rule of Supercritical CO2 Flooding in Low Permeability Cores." Applied Sciences 15, no. 2 (2025): 730. https://doi.org/10.3390/app15020730.

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Nuclear magnetic resonance (NMR) and high-pressure mercury injection (HPMI) have been widely used as common characterization methods of pore-throat. It is generally believed that there is a power function relationship between transverse relaxation time (T2) and pore-throat radius (r), but the segmentation process of the pore-throat interval is subjective, which affects the conversion accuracy. In this paper, ordered clustering is used to improve the existing segmentation method of the pore-throat interval, eliminate the subjectivity in the segmentation process, and obtain a more accurate distr

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LIN, WEI, XIZHE LI, ZHENGMING YANG, et al. "A NEW IMPROVED THRESHOLD SEGMENTATION METHOD FOR SCANNING IMAGES OF RESERVOIR ROCKS CONSIDERING PORE FRACTAL CHARACTERISTICS." Fractals 26, no. 02 (2018): 1840003. http://dx.doi.org/10.1142/s0218348x18400030.

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Based on the basic principle of the porosity method in image segmentation, considering the relationship between the porosity of the rocks and the fractal characteristics of the pore structures, a new improved image segmentation method was proposed, which uses the calculated porosity of the core images as a constraint to obtain the best threshold. The results of comparative analysis show that the porosity method can best segment images theoretically, but the actual segmentation effect is deviated from the real situation. Due to the existence of heterogeneity and isolated pores of cores, the por

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Qin, Changfeng, Jie Zhang, Yu Duan, et al. "Research on Soil Pore Segmentation of CT Images Based on MMLFR-UNet Hybrid Network." Agronomy 15, no. 5 (2025): 1170. https://doi.org/10.3390/agronomy15051170.

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Accurate segmentation of soil pore structure is crucial for studying soil water migration, nutrient cycling, and gas exchange. However, the low-contrast and high-noise CT images in complex soil environments cause the traditional segmentation methods to have obvious deficiencies in accuracy and robustness. This paper proposes a hybrid model combining a Multi-Modal Low-Frequency Reconstruction algorithm (MMLFR) and UNet (MMLFR-UNet). MMLFR enhances the key feature expression by extracting the image low-frequency signals and suppressing the noise interference through the multi-scale spectral deco

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Reimers, I. A., I. V. Safonov, and I. V. Yakimchuk. "Segmentation of 3D FIB-SEM data with pore-back effect." Journal of Physics: Conference Series 1368 (November 2019): 032015. http://dx.doi.org/10.1088/1742-6596/1368/3/032015.

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15

Wong, H. S., M. K. Head, and N. R. Buenfeld. "Pore segmentation of cement-based materials from backscattered electron images." Cement and Concrete Research 36, no. 6 (2006): 1083–90. http://dx.doi.org/10.1016/j.cemconres.2005.10.006.

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Zhang, Feng, Ghislain Bournival, Hamed Lamei Ramandi, and Seher Ata. "Digital Cake Analysis: A Novel Coal Filter Cake Examination Technique Using Microcomputed Tomography." Minerals 13, no. 12 (2023): 1509. http://dx.doi.org/10.3390/min13121509.

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Filtration is crucial for separating solids and liquids in various industries. Understanding slurry properties and filter cake structures is essential for optimising filtration performance. Conventional methods focus on interpreting filtration data to improve the understanding of filtration mechanisms. However, examining fragile filter cakes is challenging, and current techniques often alter their structure. Conventional methods only provide an average representation of cake porosity, neglecting variations in porosity and pore distribution across the cake thickness. This study introduces the D

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Ramandi, Hamed Lamei, Peyman Mostaghimi, Ryan T. Armstrong, et al. "Pore scale imaging and modelling of coal properties." APPEA Journal 55, no. 2 (2015): 468. http://dx.doi.org/10.1071/aj14103.

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A key parameter in determining the productivity and commercial success of coal seam gas wells is the permeability of individual seams. Laboratory testing of core plugs is commonly used as an indicator of potential seam permeability. The highly heterogeneous and stress-dependent nature of coal makes laboratory measurements difficult to perform and the results difficult to interpret. Consequently, permeability in coal is poorly understood. The permeability of coal at the core scale depends on the geometry, topology, connectivity, mineralisation and spatial distribution of the cleat system, and a

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Heylen, Rob, Aditi Thanki, Dries Verhees, et al. "3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts." Measurement Science and Technology 33, no. 4 (2022): 045602. http://dx.doi.org/10.1088/1361-6501/ac459a.

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Abstract X-ray computed tomography (X-CT) plays an important role in non-destructive quality inspection and process evaluation in metal additive manufacturing, as several types of defects such as keyhole and lack of fusion pores can be observed in these 3D images as local changes in material density. Segmentation of these defects often relies on threshold methods applied to the reconstructed attenuation values of the 3D image voxels. However, the segmentation accuracy is affected by unavoidable X-CT reconstruction features such as partial volume effects, voxel noise and imaging artefacts. Thes

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ZHU, QINGYONG, WEIBIN YANG, and HUAIZHONG YU. "STUDY ON THE PERMEABILITY OF RED SANDSTONE VIA IMAGE ENHANCEMENT." Fractals 25, no. 06 (2017): 1750055. http://dx.doi.org/10.1142/s0218348x17500554.

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Scanning electron microscopy (SEM) is of great importance for studying fractal permeability. In this work, we presented a new technique, by applying the high-order upwind compact difference schemes to solve the hyperbolic conservation laws, to enhance textural differences for accurate segmentation of the SEM images. From the enhanced SEM images, the channels and pores can be obtained by using the two-stage image segmentation. Combining with the box counting method, the key parameters for evaluation of the fractal permeability such as the tortuosity fractal dimension, the pore area fractal dime

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Atrash, Hasan, and Felicitasz Velledits. "Phase Segmentation Optimization of Micro X-Ray Computed Tomography Reservoir Rock Images Using Machine Learning Techniques." Geosciences and Engineering 10, no. 15 (2022): 63–79. http://dx.doi.org/10.33030/geosciences.2022.15.063.

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We studied the performance and accuracy of some basic segmentation techniques in the analysis of the pore space and matrix voxels obtained from a 3D volume of X-ray tomographic (XCT) grayscale rock images. The segmentation and classification accuracy of unsupervised (K-means, modified Fuzzy c-means, Minimum cross-entropy, and Type-2 fuzzy entropy) and supervised Naïve Bayes methods were tested using an XCT tomogram of a carbonate reservoir rock. K-fold- cross-validation techniques were applied in the evaluation of the accuracy of the unsupervised and supervised machine learning classifiers. Th

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Patmonoaji, Anindityo, Kento Tsuji, and Tetsuya Suekane. "Pore-throat characterization of unconsolidated porous media using watershed-segmentation algorithm." Powder Technology 362 (February 2020): 635–44. http://dx.doi.org/10.1016/j.powtec.2019.12.026.

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Varghese, Anjli, Sahil Jain, Malathy Jawahar, and A. Amalin Prince. "Auto-pore segmentation of digital microscopic leather images for species identification." Engineering Applications of Artificial Intelligence 126 (November 2023): 107049. http://dx.doi.org/10.1016/j.engappai.2023.107049.

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Xia, Zhongyi, Boqi Wu, C. Y. Chan, Tianzhao Wu, Man Zhou, and Ling Bing Kong. "Deep-learning-based pyramid-transformer for localized porosity analysis of hot-press sintered ceramic paste." PLOS ONE 19, no. 9 (2024): e0306385. http://dx.doi.org/10.1371/journal.pone.0306385.

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Scanning Electron Microscope (SEM) is a crucial tool for studying microstructures of ceramic materials. However, the current practice heavily relies on manual efforts to extract porosity from SEM images. To address this issue, we propose PSTNet (Pyramid Segmentation Transformer Net) for grain and pore segmentation in SEM images, which merges multi-scale feature maps through operations like recombination and upsampling to predict and generate segmentation maps. These maps are used to predict the corresponding porosity at ceramic grain boundaries. To increase segmentation accuracy and minimize l

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Hu, Qi Zhi, Jing Xia Wang, and Gao Liang Tao. "Quantitative Analysis of Soft Soil Microstructure in Unloading Levels." Applied Mechanics and Materials 401-403 (September 2013): 1529–33. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1529.

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Quantitative analysis of soft soil microstructure in unloading levels are made by using scanning electron microscope (SEM) images, IPP and PS of image technology ,which includes image segmentation, pore size measuring and counting, three dimensional simulation of soft soil microstructure, etc. The results indicate that, with the increase of unloading grade, pore number and area of big aperture are in a sharp increase, the corresponding porosity also in ascension, so the deformation of the soil is mainly due to the change of pore; compared with the apparent 3d images of soil under the transvers

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Tsvetkov, A. V. "Object segmentation in images with complex background and morphology in the example of pore segmentation on microfiltration membranes." Pattern Recognition and Image Analysis 21, no. 3 (2011): 556–59. http://dx.doi.org/10.1134/s1054661811021082.

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Zhang, Hao, Hewen Liu, and Jinyong Bai. "Research on image recognition method of rock and soil porous media based on dithering algorithm." E3S Web of Conferences 283 (2021): 01025. http://dx.doi.org/10.1051/e3sconf/202128301025.

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Rock-soil mass is a kind of material with complex internal structure, and its macro-mechanical response and failure process are influenced by internal microscopic composition and structure. Based on the research results of digital image technology in quantitative aspects of internal structure of rock and soil, a method for segmentation of rock and soil pore images based on dithering algorithm and statistical method for multiple parameters of pores is proposed in this paper. The result of verification shows that the pore recognition method proposed in this paper is reliable, can obtain the pore

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Gou, Dazhao, Xizhong An, and Runyu Yang. "DEM investigation of the effect of particle breakage on compact properties." EPJ Web of Conferences 249 (2021): 07004. http://dx.doi.org/10.1051/epjconf/202124907004.

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Particle breakage during compaction affects compaction behavior and the quality of the formed compact. This work conducted a numerical study based on the discrete element method (DEM) to investigate the effect of particle breakage on compaction dynamics and compact properties, including particle size and density distributions, and pore properties. A force-based breakage criterion and Apollonian sphere packing algorithm were employed to characterize particle breakage behavior. The pore structures of the compacts were characterized by the watershed pore segmentation method. Calibrated with exper

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Song, Wenlong, Junyu Li, Kexin Li, Jingxu Chen, and Jianping Huang. "An Automatic Method for Stomatal Pore Detection and Measurement in Microscope Images of Plant Leaf Based on a Convolutional Neural Network Model." Forests 11, no. 9 (2020): 954. http://dx.doi.org/10.3390/f11090954.

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Stomata are microscopic pores on the plant epidermis that regulate the water content and CO2 levels in leaves. Thus, they play an important role in plant growth and development. Currently, most of the common methods for the measurement of pore anatomy parameters involve manual measurement or semi-automatic analysis technology, which makes it difficult to achieve high-throughput and automated processing. This paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks. The pro

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Yan, Ganggang, Wende Yan, Yingzhong Yuan, Xiujun Gong, Ziqi Tang, and Bai Xueyuan. "Research on the Method of Evaluating the Pore Throat Structure of Rock Microscopically Based on the 3D Pore Network Model of Digital Core." International Journal of Petroleum Technology 9 (December 5, 2022): 44–53. http://dx.doi.org/10.54653/2409-787x.2022.09.6.

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In order to solve the problems of time-consuming, poor repeatability and inability to directly reflect the pore structure of the core by traditional experimental methods to obtain the reservoir parameters, a method was proposed to study the pore structure of inner core using digital core and pore network model. Firstly, the core CT scan image is processed by filtering and denoising, threshold segmentation and pore-throat network skeleton extraction. Then, the digital core and pore network model are constructed by digital image technology and maximum sphere algorithm, and the core physical para

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Qin, Qiu Ju, Xin Jian Qiang, Ye Liu, and Jing Yang. "Rock Image Pore Identification Based on Fuzzy C-Means Clustering and Neural Networks." Applied Mechanics and Materials 571-572 (June 2014): 803–6. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.803.

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In order to realize the recognition automation of rock section pore images, a method combined Fuzzy C-Means clustering with BP neural network is proposed to recognize the pore of rock images. Firstly, Fuzzy C-Means clustering as segmentation algorithm are applied to the rock images and they are divided into two types, then using the BP neural network training and classification recognition. It is shown that the trained BP neural network can accurately identify the effective porosity in the casting image, and lay a good foundation for practical applications.

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Bennai, F., M. Hattab, Y. Ding, M. Jrad, and J. Guyon. "Pore Network 3D Reconstruction for Clays Using FIB-SEM Images." IOP Conference Series: Earth and Environmental Science 1480, no. 1 (2025): 012079. https://doi.org/10.1088/1755-1315/1480/1/012079.

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Abstract The aim of this study is to propose a new approach to explore the 3D evolution of pore properties in a saturated and remoulded clay in relation to mechanical loading. Oedometric loading until the desired stress level was applied to a clay sample. A post-mortem observation using scanning electron microscopy (SEM) coupled with focused ion beam (FIB) was used to obtain the 3D microstructure of a micro-volume of the sample. A novel processing method, incorporating machine learning for pore segmentation was performed to reconstitute the micro-volume. Pore network properties were studied th

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Shen, Z. F., X. X. Li, X. J. Yi, Z. H. Wang, and H. M. Gao. "Preliminary study on the mechanical behavior of clay in one-dimensional compression with DEM simulations." IOP Conference Series: Earth and Environmental Science 1330, no. 1 (2024): 012039. http://dx.doi.org/10.1088/1755-1315/1330/1/012039.

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Abstract The mechanical behavior and physical properties of clay are closely associated with its microstructure. Current research on the macroscopic mechanics and physical properties of clay is comprehensive and systematic. However, the microstructural variations underlying these characteristics have been predominantly examined under mercury intrusion porosimetry and scanning electron microscopy, while quantitative and systematic studies are notably limited. This research employs the discrete element method (DEM) simulation to investigate the microscopic responses of clay, simulating one-dimen

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Arthur, Aviz Palma e. Silva, Maria Silva Capuzzo Valdirene, Fonseca da Silva Eugênia, and Maués Brabo Pereira André. "REFINING POROSITY ANALYSIS IN HIGH-STRENGTH MICROCONCRETES THROUGH MANUAL THRESHOLD SEGMENTATION IN X-RAY MICROTOMOGRAPHY." International Journal of Advances in Engineering & Technology (IJAET) 16, no. 3 (2023): 46–56. https://doi.org/10.5281/zenodo.8125864.

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<em>The present study aimed to assess the effectiveness of the image segmentation technique utilizing the Threshold method for the measurement of porosity in high-strength microconcrete samples that incorporate metakaolin. Specifically, X-ray microtomography testing was conducted on the microconcrete samples after 7 days of curing, and the voxel size was set at 20μm. Subsequently, the images were subjected to pre-processing measures including histogram regularization and noise reduction. Thereafter, the regions of interest within the images were demarcated through segmentation, enabling the

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Tang, Xin, Ruiyu He, Biao Wang, Yuerong Zhou та Hong Yin. "Intelligent Identiﬁcation and Quantitative Characterization of Pores in Shale SEM Images Based on Pore-Net Deep-Learning Network Model". Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 65, № 2 (2024): 233–45. http://dx.doi.org/10.30632/pjv65n2-2024a6.

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Among the various shale reservoir evaluation methods, the scanning electron microscope (SEM) image method is widely used. Its image can intuitively reflect the development stage of a shale reservoir and is often used for the qualitative characterization of shale pores. However, manual image processing is inefficient and cannot quantitatively characterize pores. The semantic segmentation method of deep learning greatly improves the efficiency of image analysis and can calculate the face rate of shale SEM images to achieve quantitative characterization. In this paper, the high-maturity shale of

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Kazak, Andrey, Kirill Simonov, and Victor Kulikov. "Machine-Learning-Assisted Segmentation of Focused Ion Beam-Scanning Electron Microscopy Images with Artifacts for Improved Void-Space Characterization of Tight Reservoir Rocks." SPE Journal 26, no. 04 (2021): 1739–58. http://dx.doi.org/10.2118/205347-pa.

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Summary The modern focused ion beam-scanning electron microscopy (FIB-SEM) allows imaging of nanoporous tight reservoir-rock samples in 3D at a resolution up to 3 nm/voxel. Correct porosity determination from FIB-SEM images requires fast and robust segmentation. However, the quality and efficient segmentation of FIB-SEM images is still a complicated and challenging task. Typically, a trained operator spends days or weeks in subjective and semimanual labeling of a single FIB-SEM data set. The presence of FIB-SEM artifacts, such as porebacks, requires developing a new methodology for efficient i

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Choi, Chae-Soon, Yong-Ki Lee, and Jae-Joon Song. "Equivalent Pore Channel Model for Fluid Flow in Rock Based on Microscale X-ray CT Imaging." Materials 13, no. 11 (2020): 2619. http://dx.doi.org/10.3390/ma13112619.

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Pore-scale modeling with a reconstructed rock microstructure has become a dominant technique for fluid flow characterization in rock thanks to technological improvements in X-ray computed tomography (CT) imaging. A new method for the construction of a pore channel model from micro-CT image analysis is suggested to improve computational efficiency by simplifying a highly complex pore structure. Ternary segmentation was applied through matching a pore volume experimentally measured by mercury intrusion porosimetry with a CT image voxel volume to distinguish regions denoted as “apparent” and “ind

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Ji, Yun Tao, Patrick Baud, Teng Fong Wong, and Li Qiang Liu. "Quantitative Characterization of 3D Pore Structure in Porous Limestone." Advanced Materials Research 671-674 (March 2013): 1830–34. http://dx.doi.org/10.4028/www.scientific.net/amr.671-674.1830.

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The pore structure in intact and inelastically deformed Indiana limestone have been studied using x-ray microtomography imaging. Guided by detailed microstructural observations and using Multi-level Otsu’s thresholding method, the 3D images acquired at voxel side length of 4 μm were segmented into three domains: solid grains, macropores and an intermediate zone dominated by microporosity. Local Porosity can be defined to infer the porosity of each voxel. The macropores were individually identified by morphological processing and their shape quantified by their sphericity and equivalent diamete

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Van Eyndhoven, G., M. Kurttepeli, C. J. Van Oers, et al. "Pore REconstruction and Segmentation (PORES) method for improved porosity quantification of nanoporous materials." Ultramicroscopy 148 (January 2015): 10–19. http://dx.doi.org/10.1016/j.ultramic.2014.08.008.

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Li, Jiaming, Xiaoxun Zhang, Fang Ma, Shuxian Wang, and Yuanyou Huang. "Simultaneous Pore Detection and Morphological Features Extraction in Laser Powder Bed Fusion with Image Processing." Materials 17, no. 6 (2024): 1373. http://dx.doi.org/10.3390/ma17061373.

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Internal pore defects are inevitable during laser powder bed fusion (LPBF), which have a significant impact on the mechanical properties of the parts. Therefore, detecting pores and obtaining their morphology will contribute to the quality of LPBF parts. Currently, supervised models are used for defect image detection, which requires a large amount of LPBF sample data, image labeling, and computing power equipment during the training process, resulting in high detection costs. This study extensively collected LPBF sample data and proposed a method for pore defect classification by obtaining it

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Zhao, Keying, and Zhanghua Zhang. "NMR and SEM fractal dimensions explore shale pore structure taking the Upper Paleozoic shale in Ordos Basin as an example." PLOS One 20, no. 5 (2025): e0323968. https://doi.org/10.1371/journal.pone.0323968.

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In this paper, the fractal dimension is calculated by extracting pore parameters from SEM images and NMR experimental data, the pore structure heterogeneity in plane and space is comprehensively discussed, and the relationship between the fractal dimension and shale composition and physical parameters is discussed, providing new ideas for the study of shale reservoirs heterogeneity. Fractal dimension analysis of SEM images reveals that the shale pores of the Shanxi Formation can be divided into organic pores, inter-granular pores and micro-fractures. The average diameter of nano-scale pores is

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Hagenmuller, Pascal, Guillaume Chambon, Bernard Lesaffre, Frédéric Flin, and Mohamed Naaim. "Energy-based binary segmentation of snow microtomographic images." Journal of Glaciology 59, no. 217 (2013): 859–73. http://dx.doi.org/10.3189/2013jog13j035.

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AbstractX-ray microtomography has become an essential tool for investigating the mechanical and physical properties of snow, which are tied to its microstructure. To allow a quantitative characterization of the microstructure, the grayscale X-ray attenuation coefficient image has to be segmented into a binary ice/pore image. This step, called binary segmentation, is crucial and affects all subsequent analysis and modeling. Common segmentation methods are based on thresholding. In practice, these methods present some drawbacks and often require time-consuming manual post-processing. Here we pre

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Rabbani, Arash, Brittany Wojciechowski, and Bhisham Sharma. "Imaging based pore network modeling of acoustical materials." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A361. http://dx.doi.org/10.1121/10.0019165.

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The acoustical behavior of porous materials is dictated by their underlying pore network geometry. Given the complexity of accurately characterizing the various pore network features, current acoustical models instead rely on indirectly incorporating these features by accounting for them within acoustical transport properties, such as tortuosity, viscous and thermal characteristic lengths, and flow resistivity. In turn, these transport properties are currently identified using inverse characterization techniques or using multiphysics modeling techniques. Here, we propose the use of advanced im

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Xu, J., X. Huang, Z. Zhang, and G. Jin. "Pore structure characteristics of coral reef limestone: a combined polarizing microscope and CT scanning study." IOP Conference Series: Earth and Environmental Science 1332, no. 1 (2024): 012026. http://dx.doi.org/10.1088/1755-1315/1332/1/012026.

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Abstract Coral reef limestone is a special class of geological medium formed through long-term deposition following the death of reef-building coral groups. Because it retains the skeletal structure of marine organisms during its formation, its pore structure is hyper-developed and complex. Deciphering the pore structure of the coral reef limestone is important because it is closely related to its macroscopic physical and mechanical properties. This study conducted a comprehensive analysis of the pore structure features of two types of coral reef limestone collected from the construction site

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Yosifov, Miroslav, Patrick Weinberger, Bernhard Plank, et al. "Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network." Acta Polytechnica CTU Proceedings 42 (October 12, 2023): 87–93. http://dx.doi.org/10.14311/app.2023.42.0087.

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This study demonstrates the utilization of deep learning techniques for binary semantic segmentation of pores in carbon fiber reinforced polymers (CFRP) using X-ray computed tomography (XCT) datasets. The proposed workflow is designed to generate efficient segmentation models with reasonable execution time, applicable even for users using consumer-grade GPU systems. First, U-Net, a convolutional neural network, is modified to handle the segmentation of XCT datasets. In the second step, suitable hyperparameters are determined through a parameter analysis (hyperparameter tuning), and the paramet

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Choudhari, Khoobaram S., Pacheeripadikkal Jidesh, Parampalli Sudheendra, and Suresh D. Kulkarni. "Quantification and Morphology Studies of Nanoporous Alumina Membranes: A New Algorithm for Digital Image Processing." Microscopy and Microanalysis 19, no. 4 (2013): 1061–72. http://dx.doi.org/10.1017/s1431927613001542.

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AbstractA new mathematical algorithm is reported for the accurate and efficient analysis of pore properties of nanoporous anodic alumina (NAA) membranes using scanning electron microscope (SEM) images. NAA membranes of the desired pore size were fabricated using a two-step anodic oxidation process. Surface morphology of the NAA membranes with different pore properties was studied using SEM images along with computerized image processing and analysis. The main objective was to analyze the SEM images of NAA membranes quantitatively, systematically, and quickly. The method uses a regularized shoc

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Kwan Leung, Anthony, Jianbin Liu, and Zhenliang Jiang. "When nature meets technology: AI-informed discovery of soil-water-root physical interaction." E3S Web of Conferences 382 (2023): 21001. http://dx.doi.org/10.1051/e3sconf/202338221001.

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Nature-based solution using vegetation has been considered as a sustainable and environmentally friendly approach to improve slope performance through root reinforcement and variations of soil matric suction upon transpiration. During plant growth, roots explore soil pore space. How fundamentally the pore structure might evolve with time following root growth dynamics and how this dynamic soil-root interaction may modify the hydraulic properties of unsaturated soils remain unclear. This paper reports the use of advanced technologies including artificial intelligence (AI) to aid the discovery o

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Khimulia, V. V. "Study of structural characteristics of hydrocarbon reservoir pore space based on X-ray computed tomography images." Actual Problems of Oil and Gas, no. 43 (December 29, 2023): 44–57. http://dx.doi.org/10.29222/ipng.2078-5712.2023-43.art4.

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Digital studies of pore space and internal structure of hydrocarbon reservoir were conducted on the basis of multiscale X-ray computed tomography images and the analysis of heterogeneities, cavernosity, fracturing and bedding in the rock. Main results. 3D models of rock pore space were created on the basis of computed tomography images through segmentation. Porosity estimation based on the digital approach was performed. It was shown that the digitally obtained data are in good agreement with the results of laboratory measurements. Analysis and visualization of the structure of the main filtra

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Suo, Limin, Zhaowei Wang, Hailong Liu, Likai Cui, Xianda Sun, and Xudong Qin. "Innovative Deep Learning Approaches for High-Precision Segmentation and Characterization of Sandstone Pore Structures in Reservoirs." Applied Sciences 14, no. 16 (2024): 7178. http://dx.doi.org/10.3390/app14167178.

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The detailed characterization of the pore structure in sandstone is pivotal for the assessment of reservoir properties and the efficiency of oil and gas exploration. Traditional fully supervised learning algorithms are limited in performance enhancement and require a substantial amount of accurately annotated data, which can be challenging to obtain. To address this, we introduce a semi-supervised framework with a U-Net backbone network. Our dataset was curated from 295 two-dimensional CT grayscale images, selected at intervals from nine 4 mm sandstone core samples. To augment the dataset, we

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Zhao, Xinli, Zhengming Yang, Xuewei Liu, Zhiyuan Wang, and Yutian Luo. "Analysis of pore throat characteristics of tight sandstone reservoirs." Open Geosciences 12, no. 1 (2020): 977–89. http://dx.doi.org/10.1515/geo-2020-0121.

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AbstractThe characterization of pore throat structure in tight reservoirs is the basis for the effective development of tight oil. In order to effectively characterize the pore -throat structure of tight sandstone in E Basin, China, this study used high-pressure mercury intrusion (HPMI) testing technology and thin section (TS) technology to jointly explore the characteristics of tight oil pore throat structure. The results of the TS test show that there are many types of pores in the tight sandstone, mainly the primary intergranular pores, dissolved pores, and microfractures. Based on the pore

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Jouini, M. S., S. Vega, and E. A. Mokhtar. "Multiscale characterization of pore spaces using multifractals analysis of scanning electronic microscopy images of carbonates." Nonlinear Processes in Geophysics 18, no. 6 (2011): 941–53. http://dx.doi.org/10.5194/npg-18-941-2011.

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Abstract. Pore spaces heterogeneity in carbonates rocks has long been identified as an important factor impacting reservoir productivity. In this paper, we study the heterogeneity of carbonate rocks pore spaces based on the image analysis of scanning electron microscopy (SEM) data acquired at various magnifications. Sixty images of twelve carbonate samples from a reservoir in the Middle East were analyzed. First, pore spaces were extracted from SEM images using a segmentation technique based on watershed algorithm. Pores geometries revealed a multifractal behavior at various magnifications fro

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