Abstract
Discontinuity sets play an essential and pivotal role in the deformation monitoring and stability analysis of the rock mass, but there are still many challenges for accurately and rapidly extracting discontinuity. In this study, an extraction and characterization method of discontinuity sets based on point cloud supervoxel segmentation was proposed, which consists of four parts: 1) a multiresolution supervoxel segmentation (MRSS) algorithm was developed to classify unstructured point cloud into multiresolution facets and discrete points; 2) to extract the individual discontinuity, the single supervoxel that having spatial connectivity, similar planarity, and parallelism was clustered; 3) the orientation of individual discontinuity was calculated, respectively, based on the plane fitting parameters; and 4) for comprehensively analyzing the stability of rock mass, the improved K -means clustering algorithm is utilized to constructing the discontinuity sets that having similar orientation information. The novel method has been successfully tested on two practical cases (a rock cut and a side slope point cloud captured by the terrestrial laser scanner). A comparison with existing methods shows that the deviation of the discontinuity orientation for rock cut is less than 1°, and the time efficiency is increased by 2.6 times. In addition, the orientation variation of the seven principle discontinuity in the five temporal side slope point cloud is relatively small, the dip direction and angle are within 2° and 1°, respectively. We can conclude that the proposed method can efficiently obtain the full extent of every individual discontinuity from rock mass surface point cloud and accurately analyze their orientation information.
Highlights
I N the field of engineering geology and rock mechanics, the geomechanical behavior of rock mass is generally determined by the integral fabric of discontinuities, which are planes or surfaces characterizing changes in the physical or chemical properties and are usually manifested as joints, faults, bedding, or damage caused by construction blasting [1]-[3]
With the development of the non-contact remote sensing technologies, image-based methods for obtaining rock mass discontinuity have been highlighted by many researchers, such as digital photogrammetry [9], [10] and ground-based differential interferometric synthetic aperture radar (GBDInSAR) [11], which allows for the acquisition of highresolution data with a lower cost and with more user-friendly survey planning [12]
An automatic extraction and characterization method of rock mass discontinuity based on multiresolution supervoxel segmentation (MRSS) is proposed in our study
Summary
I N the field of engineering geology and rock mechanics, the geomechanical behavior (such as deformation, flow modeling, and reservoir characterization) of rock mass is generally determined by the integral fabric of discontinuities, which are planes or surfaces characterizing changes in the physical or chemical properties and are usually manifested as joints, faults, bedding, or damage caused by construction blasting [1]-[3]. In the work of Liu et al (2019), clustering and major orientation estimating algorithms based on voxels and Gaussian Kernel is proposed to extract rock surfaces from the unorganized point cloud [37]. (4) There is no topological relationship on the scattered point cloud captured by the 3D laser scanning technology, the boundary feature of the rock mass cannot be preserved well in complex areas Aim at these problems, an automatic extraction and characterization method of rock mass discontinuity based on multiresolution supervoxel segmentation (MRSS) is proposed in our study. (1) Instead of using the unorganized point cloud, the multiresolution supervoxel segmentation strategy is presented to represent the rock mass surfaces, and the discontinuity is extracted based on the reconstructed supervoxel, which significantly decreases the number of basic units and the search space, and the multiresolution strategy can preserve the object boundary features.
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