Digital outcrop models provide a powerful data basis to obtain orientation information on rock masses. Robust and transferrable automatic methods are required to process and analyze these data, as outcrops and hence acquired 3D point clouds are influenced by varying conditions depending e.g., on the site, atmospheric conditions and other factors. A crucial aspect in assessing the structural character of rock masses is the analysis of fractures, which can be done directly in the 3D point cloud. This new publication validates fracture data automatically extracted from 3D rock mass data:
Drews, T., Miernik, G., Anders, K., Höfle, B., Profe, J., Emmerich, A., & Bechstädt, T. (2018). Validation of fracture data recognition in rock masses by automated plane detection in 3D point clouds. International Journal of Rock Mechanics and Mining Sciences, 109, pp. 19-31. doi: 10.1016/j.ijrmms.2018.06.023.
Abstract: This paper presents (1) an automated method to extract planes and their spatial orientation directly from 3D point clouds, followed by (2) extensive validation tests accompanied by thorough statistical analysis, and (3) a fracture intensity calculation on automatically segmented planes. For the plane extraction, a region growing segmentation algorithm controlled by several input parameters is applied to a point cloud of a granite outcrop. Within its complex surface shape, more than 1000 compass measurements were conducted for validation. In addition, digitally handpicked planes in the software Virtual Reality Geological Studio (VRGS) were used for single plane comparison. In a second test site, we performed fracture intensity calculation in Petrel based on results of the segmentation algorithm on mechanical layers of a clastic sedimentary succession. The comparison of automated segmentation results and compass measurements of three different plane sets shows a deviation of 0.70–2.00°, while the mean single plane divergence amounts to 4.97°. Hence, this study presents a fast, precise, and highly adaptable automated plane detection method, which is reproducible, transparent, objective, and provides increased accuracy in outcrops with rough and complex surfaces. Moreover, output formats of spatial orientation and location of planes are designed for simple handling in other workflows and software.
Related publication with further details on the automatic plane segmentation:
Anders, K., Hämmerle, M., Miernik, G., Drews, T., Escalona, A., Townsend, C., & Höfle, B. (2016). 3D Geological Outcrop Characterization: Automatic Detection of 3D Planes (Azimuth and Dip) Using LiDAR Point Clouds. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Science, III-5, pp. 105-112. doi: 10.5194/isprs-annals-III-5-105-2016.