In this study, the use of automated discontinuity orientation analysis is evaluated as a potential addition to manual data collection. Point clouds of outcrops of three different rock masses in Germany with distinct structural characteristics are analysed using Open-Source Software Cloud Compare and the plug-in Facets. The accuracy of the automated analysis data is compared to manually collected data. The findings of this study confirm that the discontinuity orientation from automated analysis corresponds to the manually generated data regardless of the geological setting but shows differences caused by exposed area of the discontinuity surfaces and undetected discontinuities due to minimal apertures. Further there remain differences between the results of the methods caused by complex morphology, especially in the context of human bias. The automated approach allows for the investigation of areas that are inaccessible by manual methods, and can also reduce human bias through careful interpretation of the results.
Automated discontinuity analysis can provide interpretation of 3D geological data and complement manual surveys when outcrops are not fully accessible, e.g., due to vegetation, stability problems or steep and high slopes. Also, automated data collection may provide a quick, yet comprehensive alternative when access to the region of interest is temporally limited, e.g., during tunnelling. During the past two decades Digital Outcrop Models (DOMs) have mainly been created using LiDAR or laser scanning technology (Thiele et al. 2017; Herrero et al. 2022). An alternative approach is the use of unmanned arial vehicles (UAVs) due to low costs, user-friendliness and applicability (Dewez et al. 2016; Jordá Bordehore et al. 2017), especially for generating DOMs of smaller areas (Herrero et al. 2022). According to previous studies, the automated analysis of discontinuity orientations largely agree with manual data (Herrero et al. 2022; Dewez et al. 2016; Monsalve et al. 2021), but inaccuracies have been reported: a) The absolute number of measured values per joint set scales with the area of discontinuities represented in the 3D model, thus statistical analyses in stereographic projections can be misleading due to the larger amount of data at the -apparently-largest discontinuity plane (Dewez et al. 2016). b) Discontinuities may not be exposed well enough for automatic determination, especially when apertures are small. Thus, entire joint sets may remain unidentified (Herrero et al. 2022).
