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

Laser 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.