This paper briefly introduces a series of workflows for automated digital geological mapping based on 3D photogrammetry. The 3D photogrammetry technique can produce a detailed 3D digital model of the area of interest including point cloud data to characterize the geometric features of geological structures in the scene and image data to characterize the visual features in the scene. The proposed workflow uses double-nested mean-shift clustering and region growth to extract discontinuity surfaces from point clouds. Discontinuities traces can be extracted from image data using a hybrid global and local threshold method and integrating a series of image-processing algorithms. The trace's corresponding coordinates are acquired by linking the pixel locations corresponding to the identified traces in an image to the 3D coordinates in the point cloud based on fusing the point cloud data and image data. Finally, geological analysis can be performed to accurately measure the spatial geometry characteristics of the mapped discontinuities.
Discontinuity mapping is a fundamental task for rock mass characterization (Barton et al., 1974; ISRM, 1978; Priest, 1993; Kulatilake and Wu, 1984; Mouldon, 1998; Zhang and Einstein, 1998; Li et al., 2014; Zhu et al., 2014). Rock discontinuities in outcrops can appear in the form of planar surfaces or embedded traces as shown in Fig. 1. Measuring or mapping the properties of rock discontinuities is difficult, time-consuming, and often dangerous when using traditional field mapping and hand-held direct measuring devices (Ferrero et al., 2009).
Several recent techniques enable the construction of 3D point clouds to rapidly obtain 3D geometric information and 2D image data to obtain 2D texture information about inaccessible terrain and rock exposures, such as photogrammetry (Roncella et al., 2004; Sturzenegger and Stead, 2009; Gigi and Casagli, 2011; Tannant 2015; Li et al., 2016). The virtual geometry and texture of a rock face represented by point cloud data and image data allow an engineer to extract discontinuity information on a computer with the aid of mathematical algorithms. However, an intractable technical bottleneck is that improvements in automated extraction of rock discontinuity parameters are needed to gain maximum value from these surface models (Vöge et al., 2013).