Detecting micro fractures: a comprehensive comparison of conventional and machine-learning-based segmentation methods
Detecting micro fractures: a comprehensive comparison of conventional and machine-learning-based segmentation methods
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DOI：
10.5194/se-13-1475-2022
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发表时间：
2021-03
期刊：
影响因子：
3.4
通讯作者：
Dongwon Lee;N. Karadimitriou;M. Ruf;H. Steeb
中科院分区：
文献类型：
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作者：
Dongwon Lee;N. Karadimitriou;M. Ruf;H. Steeb
抽象的。用X射线计算机断层扫描（XRCT）研究多孔岩石已被确立为多孔介质中流动和输运的非破坏性表征的标准程序。尽管XRCT领域最近取得了进展，但由于所产生的数据中固有的噪声和成像伪影，各种挑战仍然存在。当目标是识别裂缝和/或裂缝网络时，这些问题变得更加深刻。一个挑战是感兴趣区域和相邻区域之间的对比度有限，这主要归因于裂缝的微小孔径。为了克服这一挑战，常用的方法是应用各种数字图像处理步骤，例如滤波，以提高信噪比。此外，基于阈值/形态学方案的分割方法已被用于从感兴趣的特征中获得增强的信息。然而，该工作流程需要熟练的操作员来微调其输入参数，并且由于可用方法的复杂性和XRCT数据集的大体积，所需的计算时间显著增加。在这项研究中，基于一个数据集的成功可视化的裂缝网络在卡拉拉大理石与微X射线计算机断层扫描（μXRCT），我们提出了五个分割方法，三个传统的和两个基于机器学习的结果。目的是为感兴趣的读者提供现有方法之间的全面比较，同时介绍操作原理，优点和局限性，以作为个性化分割工作流程的指南。从所有五种方法的分割结果进行了比较，彼此的质量和时间效率。由于内存的限制，为了实现公平的比较，所有的方法都采用2D方案。2D U网模型是采用的基于机器学习的分割方法之一，其输出在分割质量和所需处理时间方面表现最佳。
Abstract. Studying porous rocks with X-ray computed tomography (XRCT) has been established as a standard procedure for the non-destructive characterization of flow and transport in porous media. Despite the recent advances in the field of XRCT, various challenges still remain due to the inherent noise and imaging artifacts in the produced data. These issues become even more profound when the objective is the identification of fractures and/or fracture networks. One challenge is the limited contrast between the regions of interest and the neighboring areas, which can mostly be attributed to the minute aperture of the fractures. In order to overcome this challenge, it has been a common approach to apply various digital image processing steps, such as filtering, to enhance the signal-to-noise ratio. Additionally, segmentation methods based on threshold/morphology schemes have been employed to obtain enhanced information from the features of interest. However, this workflow needs a skillful operator to fine-tune its input parameters, and the required computation time significantly increases due to the complexity of the available methods and the large volume of an XRCT dataset. In this study, based on a dataset produced by the successful visualization of a fracture network in Carrara marble with micro X-ray computed tomography (μXRCT), we present the results from five segmentation methods, three conventional and two machine-learning-based ones. The objective is to provide the interested reader with a comprehensive comparison between existing approaches while presenting the operating principles, advantages and limitations, to serve as a guide towards an individualized segmentation workflow. The segmentation results from all five methods are compared to each other in terms of quality and time efficiency. Due to memory limitations, and in order to accomplish a fair comparison, all the methods are employed in a 2D scheme. The output of the 2D U-net model, which is one of the adopted machine-learning-based segmentation methods, shows the best performance regarding the quality of segmentation and the required processing time.