An automated fracture trace detection technique using the complex shearlet transform

Abstract

Abstract. Representing fractures explicitly using a discrete fracture network (DFN) approach is often necessary to model the complex physics that govern thermo-hydro-mechanical-chemical processes (THMC) in porous media. DFNs find applications in modelling geothermal heat recovery, hydrocarbon exploitation, and groundwater flow. It is advantageous to construct DFNs from photogrammetry of fractured outcrop analogues as the DFNs would capture realistic, fracture network properties. Recent advances in drone photogrammetry have greatly simplified the process of acquiring outcrop images, and there is a remarkable increase in the volume of image data that can be routinely generated. However, manually digitizing fracture traces is tedious and inevitably subject to interpreter bias. Additionally, variations in interpretation style can result in different fracture network geometries, which, may then influence modelling results depending on the use-case of the fracture study. In this paper, an automated fracture trace detection technique is introduced. The method consists of ridge detection using the complex shearlet transform coupled with post-processing algorithms that threshold, skeletonize, and vectorize fracture traces. The technique is applied to the task of automatic trace extraction at varying scales of rock discontinuities, ranging from 100–102 m. We present automatic trace extraction results from three different fractured outcrop settings. The results indicate that the automated approach enables extraction of fracture patterns at a volume beyond what is manually feasible. Comparative analysis of automatically extracted results with manual interpretations demonstrates that the method can eliminate the subjectivity that is typically associated with manual interpretation. The proposed method augments the process of characterizing rock fractures from outcrops.

An Advanced Discrete Fracture Methodology for Fast, Robust, and Accurate Simulation of Energy Production From Complex Fracture Networks

“…The other problem is the scale of the image. The Brejoes data set has a huge resolution (20 mm/pixel; Prabhakaran et al., 2019). It can be argued that you would roughly need 15–25 pixels to be sure about the interaction of two or more fractures due to shading, contrast, and other optical effects in the image.…”

“…Both networks are interpreted by hand; however, the developed method would also be very suitable for automatic fracture detection algorithms as presented in Prabhakaran et al. (2019). The synthetic test case consists of a high‐permeable matrix and low‐permeable fractures with a narrow opening in the middle of the domain.…”

Investigating Spatial Heterogeneity within Fracture Networks using Hierarchical Clustering and Graph Distance Metrics

“…2 with the associated dendrogram structure. To validate the proposed approach based on graph distance metrics and hierarchical clustering, we utilize a 2D fracture dataset from the Lilstock pavement in the Bristol Channel, UK (Prabhakaran et al, 2021b automatically traced using a technique described in Prabhakaran et al (2019) from UAV photogrammetric data published by . The fracture networks correspond to Jurassic limestones with very dense joint networks spread across multiple layers.…”

“…2D fracture trace maps are especially useful as this type of data combines both geometric and topological information in the form of a network. Recent advances in UAV-photogrammetry (Bemis et al, 2014;Bisdom et al, 2017) and automated image processing algorithms (Prabhakaran et al, 2019) have led to large datasets of 2D fracture traces that reveal much more about network attributes than is possible from 1D sampling. Given such large datasets with rich information, it is pertinent to directly quantify spatial variation from the network structure.…”

Adaptive Segmentation for Discontinuity Detection on Karstified Carbonate Outcrop Images From UAV-SfM Acquisition and Detection Bias Analysis

“…For this, signal analysis methods like Phase Congruence [20], Wavelet [21]- [23], Shearlet [24], [25], line detection with Hough transform [26], [27], ridge and edge detection filters with Canny [20], [26]- [30] and Steger algorithm [27], [30] have also being applied in fracture detection. With most of these methods generating noisy images when detecting discontinuities in highly fractured outcrop or with irregular shadowed areas and vegetation.…”