A complete algorithm for starting with a digital image of a rock face, automatically delineating traces, and extracting three dimensional joint or fracture orientation data is described. Less emphasis is placed on the pre-processing of the image, as different images require different levels of filtering, but nearly all pre-pmcessing ultimately produces a binary image resembling a trace map. The special R-Theta Hough transform is then used to find and extract linear features from the binary image. Using relationships derived from vector calculus, and assuming a Fisher distrilbution for each structure set, a forward Monte Carlo simulation is performed to determine mean dip and dip direction of the set, as well as the Fisher constant, K. A case study is presented to illustrate the performance of the algorithm, however more case studies are needed to compare results gained with image processing to results of a scanline survey.
Good data on joint and fracture geometry means the difference between success and failure in many scientific and engineering rock mechanics projects. However, collecting detailed information in the field requires time-consuming surveys, sometimes at great expense. In addition to time constraints, other factors can make it difficult to collect field data such as safety of working conditions, access to outcrops in the case of steep slopes, and the volume of data required for a meaningful statistical sampling. Typically, rock joint information can only be extracted from the bottom 2 3 m of an outcrop using traditional field methods. It will always be necessary to visit field sites and collect data, but extracting joint geometries from digital images could greatly speed up the process, as well as acquire information unavailable from traditional methods.