Automated rock mass discontinuity set characterisation using amplitude and phase decomposition of point cloud data

Article

Singh, Sarvesh Kumar, Banerjee, Bikram Pratap, Lato, Matthew J., Sammut, Claude and Raval, Simit. 2022. "Automated rock mass discontinuity set characterisation using amplitude and phase decomposition of point cloud data." International Journal of Rock Mechanics and Mining Sciences. 152, pp. 1-17. https://doi.org/10.1016/j.ijrmms.2022.105072
Article Title

Automated rock mass discontinuity set characterisation using amplitude and phase decomposition of point cloud data

ERA Journal ID4029
Article CategoryArticle
AuthorsSingh, Sarvesh Kumar, Banerjee, Bikram Pratap, Lato, Matthew J., Sammut, Claude and Raval, Simit
Journal TitleInternational Journal of Rock Mechanics and Mining Sciences
Journal Citation152, pp. 1-17
Article Number105072
Number of Pages17
Year2022
PublisherElsevier
Place of PublicationUnited Kingdom
ISSN0020-7624
0148-9062
1365-1609
1873-4545
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ijrmms.2022.105072
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S1365160922000405
AbstractLaser scanning is an efficient approach for collecting rock mass point cloud scans to characterise structural discontinuities. However, developing computationally efficient and robust analytical workflows remains an open research problem. Existing semi-automated and automated approaches rely on point normals which are prone to mapping error when high variability exists in the local-support region. This study proposes a new automated algorithm that uses the spatial distribution of points on discontinuities to capture unique signatures in the form of sinusoidal waves. The discontinuities are then effectively characterised by clustering the amplitude and phase profiles of the sinusoidal waves. The presented amplitude and phase decomposition (APD) approach requires minimal pre-processing. Moreover, it can be applied directly to raw point clouds as filtering is inherently included through the fast Fourier transform (FFT) based decomposition of the signals. The method was evaluated on an underground tunnel dataset with exposed structural discontinuity planes. The efficacy of the developed approach was tested against manual segmentation using virtual compass plugin in open-source software (Cloud Compare), semi-automated open-source (discontinuity set extractor) and proprietary (Maptek PointStudio) software, and other automated algorithmic approaches based on point normal clustering and region growing. The APD approach produced the least error in estimating mean discontinuity dip angle and dip direction which was ±1.15° and ±1.39° with a dispersion error of ±2.24° and ±1.54°, respectively.
KeywordsDigital outcrop; Discontinuity characterisation; LiDAR; Stereonet; Rock mass characterisation
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020401306. Surveying (incl. hydrographic surveying)
401905. Mining engineering
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Byline AffiliationsUniversity of New South Wales
Agriculture Victoria
BGC Engineering Inc
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