[论文解读] Detecting micro fractures with X-ray computed tomography.

Dongwon Lee, Nikolaos Karadimitriou+2 more|arXiv (Cornell University)|Mar 23, 2021

Mineral Processing and Grinding参考文献 78被引用 4

一句话总结

本研究评估了五种分割方法——三种传统方法和两种基于机器学习的方法——在卡拉拉大理石X射线计算机断层扫描（XRCT）数据中检测微裂纹的表现。2D U-Net深度学习模型在分割准确性和处理速度方面均优于其他方法，尽管存在内存限制，仍表现出优越性能。

ABSTRACT

Studying porous rock materials with X-Ray Computed Tomography (XRCT) has been established as a standard procedure for the non-destructive visualization of flow and transport in opaque porous media. Despite the recent advances in the field of XRCT, some challenges still remain due to the inherent noise and imaging artefacts in the produced data. These issues become even more profound when the objective is the identification of fractures, and/or fracture networks. The challenge is the limited contrast between the regions of interest and the neighboring areas. This limited contrast 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 digital image processing, such as filtering, to enhance the signal-to-noise ratio. Additionally, segmentation methods based on threshold-/morphology schemes can be 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 the data-set. In this study, based on a data-set produced by the successful visualization of a fracture network in Carrara marble with XRCT, we present the segmentation results from a number of segmentation methods. Three conventional and two machine-learning-based methods are evaluated. The segmentation results from all five methods are compared to each other in terms of segmentation 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.

研究动机与目标

• 为解决多孔岩石XRCT数据中微裂纹与周围基质之间对比度低的挑战。
• 评估传统方法与基于机器学习的分割技术在检测微裂纹方面的表现。
• 在内存受限条件下，比较多种方法在分割质量与计算效率方面的差异。
• 确定深度学习方法是否能在微裂纹检测中超越传统阈值化与形态学方法。

提出的方法

• 采用2D U-Net卷积神经网络实现语义分割，用于XRCT数据中微裂纹的分割。
• 使用三种基于阈值化和形态学操作的传统分割方法进行对比。
• 所有方法均应用于卡拉拉大理石裂隙网络的2D XRCT数据切片，以确保计算比较的公平性。
• 通过与真实情况的视觉和定量对比评估分割质量，尽管未提供明确指标。
• 测量处理时间以评估各方法的计算效率。
• 由于内存限制，研究聚焦于2D处理，尽管原始数据为3D。

实验结果

研究问题

• RQ1 在噪声较多的XRCT数据中，基于深度学习的分割方法是否能优于传统的阈值化与形态学方法？
• RQ2 2D U-Net模型在微裂纹分割准确性方面与传统方法相比如何？
• RQ3 不同方法在分割质量与处理时间之间存在何种权衡？
• RQ4 图像噪声以及裂纹与基质之间低对比度在多大程度上影响分割性能？

主要发现

• 2D U-Net模型在所有评估方法中实现了最高的分割质量。
• 2D U-Net模型的处理时间短于其他方法，表现出更优的时间效率。
• 基于阈值化与形态学的传统方法需要大量参数调优，且由于对比度低和噪声干扰而效果较差。
• 尽管采用2D实现，U-Net模型的分割结果在定性上仍显著优于传统方法。
• 内存限制迫使采用2D方法，但U-Net在这些约束下仍表现出更高的准确性和速度。
• 本研究证实，与传统图像处理流程相比，深度学习方法在噪声较多的XRCT数据中检测微裂纹更具鲁棒性。