Research paperSolid images for geostructural mapping and key block modeling of rock discontinuities
Introduction
Rock mass engineering requires a proper understanding of site geology, rock structure, mechanical and hydrological properties. Rock outcrops consist of intact rock separated and crossed by many discontinuities. Both geometrical and mechanical characterization of intact rock properties is usually performed through laboratory tests including the quantification of compressive and tensile strengths, elastic properties, etc. Beyond these internal characteristics, describing the discontinuity structure is also a crucial input to rock fall risk analysis. According to the rock block theory (Goodman and Shi, 1985), geometrical characteristics of these discontinuities, visible at the surface of the outcrop and extrapolated to the inner part of the massif, leads to the individualization of stones, blocks and masses potentially generating disorders with variable consequences depending on their localization and their fall energy. When determining the most adapted reinforcement method (rock anchors, detection nets, etc.), fracture mapping is therefore a fundamental first step in the design process. Today, cell mapping or scan line survey (Priest and Hudson, 1981) are generally based on manual compass clinometer and tape measuring. Unfortunately, manual field survey methods have several well-known weaknesses (Kemeny and Post, 2003, Slob et al., 2005).
Digital imaging and 3D laser-scanning offer the possibility to mitigate these gaps by providing a complete and accurate 3D geometric description of the surface of the digitized outcrop. Terrestrial laser-scanning technology, also known as LiDAR (for Light Detection And Ranging) and digital photogrammetry are close-range remote sensing technologies which allow rock outcrops to be digitally captured in a very short time and with unprecedented resolution and accuracy (Buckley et al., 2008, Sturzenegger and et Stead, 2009). Resulting 3D models can then be post-processed thanks to automated or semi-automated procedures for rock discontinuity characterization and exploited for the 3D documentation of any part of a rock face. Such dense 3D data, especially LiDAR point clouds, are being used ever more widely, notably for determining discontinuity orientations (Lato et al., 2010, Duan et al., 2011, García-Sellés et al., 2011, Assali et al., 2014, Riquelme et al., 2014) and discontinuity spacing (Riquelme et al., 2015), even for large scale applications (Hilley et al., 2010).
However, although the acquisition phase is highly automated, managing, handling and exploiting such great amount of collected data is an arduous task and especially for non specialist users. The conversion of point clouds data into useful information for the need of rock engineering practices is therefore a crucial issue.
This paper presents our approach to overcome this issue and discusses the solid image principle as a support for geostructral mapping and key block modeling. The concept of solid image is described and its implementation into a standalone software is discussed. Various tools are tested and illustrated thanks to a case study in a limestone quarry.
Section snippets
Definition
Bornaz and Dequal (2004) introduced the notion of ''solid image'' as the enrichment of a classical 2D digital image with the corresponding 3D geometrical information, e.g. a laser-scanning or photogrammetric point cloud. For all practical purposes, it is widely accepted that the geometrical data is not basically stored into different layers-for the coordinates components -, but are preferentially supplied thanks to a single range matrix-or depth map layer-for maximizing computing capacity (see
Implemented software for creating and managing a solid image sequence
A specific software package has been developed in a combination of C++ and R1 code to create, manage and exploit solid image sequences for fracture mapping and key block modeling purposes. Implemented tools have been included in the Gaia-GeoRoc software. Gaia-GeoRoc was developed by the authors since 2012 and is intended for processing 3D point clouds and calibrated images for rock mass
Structural mapping
The complete procedure, from the camera calibration to the solid image exploitation has been performed on different sites and especially on a limestone quarry located near the town of Saint–Jeoire, in Haute–Savoie, France (Assali et al., 2014). The results obtained with the solid image approach could therefore be compared with the already established structural statement.
The laser scanning survey has been performed with a Leica HDS7000 device and a spatial density of 1600 pts/m2, i.e. with
Conclusion
This paper describes a computational approach for 3D mapping and geostructural survey and analysis thanks to the combination of 3D point clouds and 2D digital images. Based on the concept of solid image, a new software-GAIA-GeoRoc-has been developed and validated on a typical field case study. In comparison to classical survey data which are affected by many deficiencies (lack of data and sampling difficulties due to inaccessible areas, safety risk in steep sectors, etc.), the reliability of
Acknowledgments
This project was carried out as part of a doctoral research project supported by SNCF (French National Railway Company),for improving the risk management methodologies related to linear outcrops along the railway network. The authors would like to thank SNCF's Engineering and Research & Innovation Departments, as well as the engineering company IMSRN (Ground Movement and Natural Hazards Engineering) in Montbonnot-Saint Martin (Isère, France), INSA de Strasbourg, and the University of Savoie for
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