Open fractures can affect petrophysical properties of their host rock masses, as well as fluid transport and storage, so characterization of them is important to both industrial and research scientists. X-ray Computed Tomography (CT), a non-destructive technique for 3D imaging of various materials, shows such fractures well in rock samples. However, separation and characterization of fractures in CT data is complicated when a scanned sample contains narrow and intersecting fractures, because narrow fractures become blurred when thinner than the scanner resolution and their value approximates that of the matrix, and because intersecting features are difficult to individually characterize. In this paper, we present a new approach for an objective and efficient characterization of the fracture network inside CT scans of rock samples. We have developed algorithms, implemented as Python scripts, that measure fracture aperture-related parameters, and that separate connected fractures and fracture intersections within CT images of the sample. The CT images are composed of stacks of 2D images in the plane parallel to X-Y (equally spaced), where each pixel has a value related to the attenuation of the X-rays within the materials that make up the sample at that location and is generally displayed using a gray-scale colormap. As the gray values in the reconstructed images drop within fractures, our algorithm is able to identify such drops and record the lowest gray value in every drop as a Fracture Trace Point (FTP). For every FTP, parameters related to the local fracture width and the three-dimensional orientation of the FTPs surrounding it are measured. A second step involves the separation of individual fractures and their intersections points. This allows information about a number of FTP measurements on the same fracture (or intersections) to be combined to characterize that feature. We demonstrate that our methods better quantify fractures and their intersections through analysis of an experimentally-deformed granite sample, within which we characterize fracture size, orientation, and intensity. The methodologies can be also used to characterize sub-planar features in other types of datasets. Python implementations of our algorithms are freely available on GitHub repositories.

开放性裂缝会影响其宿主岩体的岩石物性，以及流体的运移和储存，因此对工业和研究科学家来说，对其进行表征至关重要。X射线计算机断层扫描（CT）是一种对各种材料进行3D成像的非破坏性技术，可以很好地在岩石样品中显示此类裂缝。但是，当扫描的样本包含狭窄且相交的裂缝时，CT数据中裂缝的分离和表征会很复杂，这是因为当狭窄的裂缝比扫描仪分辨率薄且其值接近于基质的裂缝时，狭窄的裂缝就会变得模糊不清，并且相交的特征很难单独获得表征。在本文中，我们提出了一种新的方法，用于客观有效地表征岩石样品CT扫描中的裂缝网络。我们已经开发了以Python脚本实现的算法，该算法可测量与裂缝孔径相关的参数，并在样品的CT图像内分离连接的裂缝和裂缝相交处。CT图像由平行于XY（等距）的平面中的2D图像堆栈组成，其中每个像素的值与构成该位置样品的材料中X射线的衰减有关，通常使用灰度颜色图显示。由于重建图像中的灰度值在裂缝内下降，因此我们的算法能够识别出此类液滴，并将每个液滴中的最低灰度值记录为断裂痕迹点（FTP）。对于每个FTP，都测量与局部裂缝宽度和围绕其的FTP的三维方向有关的参数。第二步涉及分离单个裂缝及其相交点。这样可以将有关同一裂缝（或相交处）的多个FTP测量的信息组合起来，以表征该特征。我们证明，通过对实验变形的花岗岩样品进行分析，我们的方法可以更好地量化裂缝及其相交处，其中我们可以表征裂缝的大小，方向和强度。该方法还可以用于表征其他类型的数据集中的次平面特征。我们算法的Python实现可在以下位置免费获得 我们证明，通过对实验变形的花岗岩样品进行分析，我们的方法可以更好地量化裂缝及其相交处，其中我们可以表征裂缝的大小，方向和强度。该方法还可以用于表征其他类型的数据集中的次平面特征。我们算法的Python实现可在以下位置免费获得 我们证明，通过对实验变形的花岗岩样品进行分析，我们的方法可以更好地量化裂缝及其相交处，其中我们可以表征裂缝的大小，方向和强度。该方法还可以用于表征其他类型的数据集中的次平面特征。我们算法的Python实现可在以下位置免费获得的GitHub 仓库。