A case study for utilization of image processing in jointed network detection in open-pit mining

This study attempted to use the image processing techniques for estimating the discontinuity network detection in dimension stones from open-pit mining work front. The mentioned processing techniques are utilized to determine the discontinuities distribution of the rock mass as geometrical properties charging instability condition in excavation process in quarry. To evaluate the discontinuity network detection from quarry work front used image processing-based algorithm were run in Python programming languages. The algorithm, first of all, identifies the discontinuities and generates the network of discontinuities emplacements to model the rock body in quarry work front. To this end, the triple processing steps included pre-processing, main processing and post-processing are conducted to model. According to the results of computer-based simulation of discontinuity network, the used algorithm is capable to detect the discontinuities emplacements in rock mass and recognize the main rock block spatial distribution in quarry work front.