Solid images for geostructural mapping and key block modeling of rock discontinuities
Résumé
Rock mass characterization is obviously a key element in rock fall hazard analysis. Managing risk and determining the most adapted reinforcement method require a proper understanding of the considered rock mass. Description of discontinuity sets is therefore a crucial first step in the reinforcement work design process. The on-field survey is then followed by a structural modeling in order to extrapolate the data collected at the rock surface to the inner part of the massif. Traditional compass survey and manual observations can be undoubtedly surpassed by dense 3D data such as LiDAR or photogrammetric point clouds. However, although the acquisition phase is quite fast and highly automated, managing, handling and exploiting such great amount of collected data is an arduous task and especially for non specialist users. In this study, we propose a combined approached using both 3D point clouds (from LiDAR or image matching) and 2D digital images, gathered into the concept of "solid image". This product is the connection between the advantages of classical true colors 2D digital images, accessibility and interpretability, and the particular strengths of dense 3D point clouds, i.e. geometrical completeness and accuracy. The solid image can be considered as the information support for carrying-out a digital survey at the surface of the outcrop without being affected by traditional deficiencies (lack of data and sampling difficulties due to inaccessible areas, safety risk in steep sectors, etc.). Computational tools presented in this paper have been implemented into one standalone software through a graphical user interface helping operators with the completion of a digital geostructural survey and analysis. 3D coordinates extraction, 3D distances and area measurement, planar best-fit for discontinuity orientation, directional roughness profiles, block size estimation, and other tools have been experimented on a calcareous quarry in the French Alps. (C) 2016 Elsevier Ltd. All rights reserved.