3D GEOLOGICAL OUTCROP CHARACTERIZATION: AUTOMATIC DETECTION OF 3D PLANES (AZIMUTH AND DIP) USING LiDAR POINT CLOUDS

Abstract

ABSTRACT:Terrestrial laser scanning constitutes a powerful method in spatial information data acquisition and allows for geological outcrops to be captured with high resolution and accuracy. A crucial aspect for numerous geologic applications is the extraction of rock surface orientations from the data. This paper focuses on the detection of planes in rock surface data by applying a segmentation algorithm directly to a 3D point cloud. Its performance is assessed considering (1) reduced spatial resolution of data and (2) smoothing in the course of data pre-processing. The methodology is tested on simulations of progressively reduced spatial resolution defined by varying point cloud density. Smoothing of the point cloud data is implemented by modifying the neighborhood criteria during normals estimation. The considerable alteration of resulting planes emphasizes the influence of smoothing on the plane detection prior to the actual segmentation. Therefore, the parameter needs to be set in accordance with individual purposes and respective scales of studies. Furthermore, it is concluded that the quality of segmentation results does not decline even when the data volume is significantly reduced down to 10%. The azimuth and dip values of individual segments are determined for planes fit to the points belonging to one segment. Based on these results, azimuth and dip as well as strike character of the surface planes in the outcrop are assessed. Thereby, this paper contributes to a fully automatic and straightforward workflow for a comprehensive geometric description of outcrops in 3D.

Geotechnical Study of Raspadalica Cliff Rockfall, Croatia

“…After the establishment of the 3D HRPC model described previously, the model was analyzed to identify the main characteristics of the rock mass structure, such as the detection and mapping of the discontinuities and discontinuity sets, orientation and dip of discontinuities, spacing of discontinuities, persistence of discontinuities, and roughness of discontinues, as well as determination of rock block volumes. Due to the increasing use of UAV photogrammetry and the SfM technique to create 3D HRPC models, various automatic and semi-automatic techniques and methods have been developed to detect and map the discontinuities and discontinuity sets [20,[22][23][24][25][26]28,31,, orientation and dip direction of discontinuities [18,20,22,23,26,28,29,31,[37][38][39][40][42][43][44][45]48,49,[54][55][56][57][59][60][61][62][63][64][65][66][67][68][69][70][71][72]…”

Tomografía Sísmica 2.5D y rasgos geológicos en la Cuenca de Urabá (Colombia)

“…Después se hizo una lista de los sismos con el nombre de las estaciones y su correspondiente tiempo para las ondas P y S (diferencia del tiempo de arribo entre las ondas S y P). Con la información de los tiempos de arribo y ubicación de las estaciones, se utilizó el método de inversión simultánea para diferentes casos: con un solo bloque homogéneo, con variación en profundidad de la velocidad (1D) y con variación de la velocidad en profundidad y lateralidad (2.5D) (Anders et al, 2016;Castillo et al, 2010).…”