Yulai Zhang , Matthew Tsang , Mark Knackstedt , Michael Turner , Shane Latham , Euan Macaulay , Rhys Pitchers
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One key step in this process, after cleat extraction, is the separation of individual cleats, without which the cleats are a connected network and statistics for different cleat sets cannot be measured. In this paper, a feature extraction-based image processing method is introduced to identify and separate distinct cleat groups from 3D X-ray μCT images. Kernels (filters) representing explicit cleat features of coal are built and cleat separation is successfully achieved by convolutional operations on 3D coal images. The new method is applied to a coal specimen with 80 mm in diameter and 100 mm in length acquired from an Anglo American Steelmaking Coal mine in the Bowen Basin, Queensland, Australia. It is demonstrated that the new method produces reliable cleat separation capable of defining individual cleats and preserving 3D topology after separation. Bedding-parallel fractures are also identified and separated, which has historically been challenging to delineate and rarely reported. A variety of cleat/fracture statistics is measured which not only can quantitatively characterize the cleat/fracture system but also can be used for DFN modeling. Finally, variability and heterogeneity with respect to the core axis are investigated. Significant heterogeneity is observed and suggests that the representative elementary volume (REV) of the cleat groups for engineering purposes may be a complex problem requiring careful consideration.</p></div>","PeriodicalId":54219,"journal":{"name":"Journal of Rock Mechanics and Geotechnical Engineering","volume":"16 1","pages":"Pages 153-166"},"PeriodicalIF":9.4000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1674775523001245/pdfft?md5=910eafe9de54780bee7bdadc9f60778d&pid=1-s2.0-S1674775523001245-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A three-dimensional feature extraction-based method for coal cleat characterization using X-ray μCT and its application to a Bowen Basin coal specimen\",\"authors\":\"Yulai Zhang , Matthew Tsang , Mark Knackstedt , Michael Turner , Shane Latham , Euan Macaulay , Rhys Pitchers\",\"doi\":\"10.1016/j.jrmge.2023.04.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Cleats are the dominant micro-fracture network controlling the macro-mechanical behavior of coal. Improved understanding of the spatial characteristics of cleat networks is therefore important to the coal mining industry. Discrete fracture networks (DFNs) are increasingly used in engineering analyses to spatially model fractures at various scales. The reliability of coal DFNs largely depends on the confidence in the input cleat statistics. Estimates of these parameters can be made from image-based three-dimensional (3D) characterization of coal cleats using X-ray micro-computed tomography (μCT). One key step in this process, after cleat extraction, is the separation of individual cleats, without which the cleats are a connected network and statistics for different cleat sets cannot be measured. In this paper, a feature extraction-based image processing method is introduced to identify and separate distinct cleat groups from 3D X-ray μCT images. Kernels (filters) representing explicit cleat features of coal are built and cleat separation is successfully achieved by convolutional operations on 3D coal images. The new method is applied to a coal specimen with 80 mm in diameter and 100 mm in length acquired from an Anglo American Steelmaking Coal mine in the Bowen Basin, Queensland, Australia. It is demonstrated that the new method produces reliable cleat separation capable of defining individual cleats and preserving 3D topology after separation. Bedding-parallel fractures are also identified and separated, which has historically been challenging to delineate and rarely reported. A variety of cleat/fracture statistics is measured which not only can quantitatively characterize the cleat/fracture system but also can be used for DFN modeling. Finally, variability and heterogeneity with respect to the core axis are investigated. 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引用次数: 0
摘要
裂隙是控制煤炭宏观力学行为的主要微裂隙网络。因此,更好地了解裂隙网络的空间特征对煤炭开采业非常重要。在工程分析中,离散断裂网络(DFN)越来越多地用于在不同尺度上对断裂进行空间建模。煤炭离散断裂网络的可靠性在很大程度上取决于输入裂隙统计数据的可信度。利用 X 射线显微计算机断层扫描 (μCT),可以通过基于图像的煤层裂隙三维 (3D) 特征来估算这些参数。在这一过程中,煤层提取后的一个关键步骤是分离单个煤层,否则煤层就是一个连接的网络,无法测量不同煤层组的统计数据。本文介绍了一种基于特征提取的图像处理方法,用于从三维 X 射线 μCT 图像中识别和分离不同的裂隙组。通过对三维煤炭图像进行卷积运算,建立了代表煤炭明显裂隙特征的核(滤波器),并成功实现了裂隙分离。新方法应用于从澳大利亚昆士兰州博文盆地的英美炼钢煤矿获取的直径为 80 毫米、长度为 100 毫米的煤炭样本。结果表明,新方法能产生可靠的裂隙分离,能够确定单个裂隙,并在分离后保留三维拓扑结构。此外,还识别并分离出了与床层平行的断裂,而这在历史上一直是难以划分且很少报道的。测量的各种裂隙/断裂统计数据不仅可以定量描述裂隙/断裂系统,还可用于 DFN 建模。最后,研究了与岩心轴线有关的变异性和异质性。观察到了显著的异质性,这表明用于工程目的的夹板组代表性基本体积(REV)可能是一个复杂的问题,需要仔细考虑。
A three-dimensional feature extraction-based method for coal cleat characterization using X-ray μCT and its application to a Bowen Basin coal specimen
Cleats are the dominant micro-fracture network controlling the macro-mechanical behavior of coal. Improved understanding of the spatial characteristics of cleat networks is therefore important to the coal mining industry. Discrete fracture networks (DFNs) are increasingly used in engineering analyses to spatially model fractures at various scales. The reliability of coal DFNs largely depends on the confidence in the input cleat statistics. Estimates of these parameters can be made from image-based three-dimensional (3D) characterization of coal cleats using X-ray micro-computed tomography (μCT). One key step in this process, after cleat extraction, is the separation of individual cleats, without which the cleats are a connected network and statistics for different cleat sets cannot be measured. In this paper, a feature extraction-based image processing method is introduced to identify and separate distinct cleat groups from 3D X-ray μCT images. Kernels (filters) representing explicit cleat features of coal are built and cleat separation is successfully achieved by convolutional operations on 3D coal images. The new method is applied to a coal specimen with 80 mm in diameter and 100 mm in length acquired from an Anglo American Steelmaking Coal mine in the Bowen Basin, Queensland, Australia. It is demonstrated that the new method produces reliable cleat separation capable of defining individual cleats and preserving 3D topology after separation. Bedding-parallel fractures are also identified and separated, which has historically been challenging to delineate and rarely reported. A variety of cleat/fracture statistics is measured which not only can quantitatively characterize the cleat/fracture system but also can be used for DFN modeling. Finally, variability and heterogeneity with respect to the core axis are investigated. Significant heterogeneity is observed and suggests that the representative elementary volume (REV) of the cleat groups for engineering purposes may be a complex problem requiring careful consideration.
期刊介绍:
The Journal of Rock Mechanics and Geotechnical Engineering (JRMGE), overseen by the Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, is dedicated to the latest advancements in rock mechanics and geotechnical engineering. It serves as a platform for global scholars to stay updated on developments in various related fields including soil mechanics, foundation engineering, civil engineering, mining engineering, hydraulic engineering, petroleum engineering, and engineering geology. With a focus on fostering international academic exchange, JRMGE acts as a conduit between theoretical advancements and practical applications. Topics covered include new theories, technologies, methods, experiences, in-situ and laboratory tests, developments, case studies, and timely reviews within the realm of rock mechanics and geotechnical engineering.