The goal of this research is to develop mine-scale discrete fracture network (DFN) models in which the influence of the spatial heterogeneity of fracture distributions may be investigated on the rock wedge stability of an open pit slope. For this purpose, spatially conditioned DFN models were developed for the pit walls at Tasiast mine using comprehensive structural data from the mine. Using Sequential Gaussian Simulation (SGS), volumetric fracture intensities (P32) were modeled across the entire mine site in the form of 3D block models. The simulated P32 block models were used as the input constraints for conditional DFN fracture generation, where the DFN grid dimension is the same as the SGS 3D blocks. The spatially constrained DFN models were further calibrated using aerial fracture intensities (P21) data from the pit walls, obtained by a survey of the pit walls using an unmanned aerial vehicle (UAV) and measured traces of joints from 3D point cloud data. The final DFN model is expected to honor the fracture intensities gathered through different means with optimal model accuracy. Finally, bench-scale and interramp scale rock wedge slope stability analyses were conducted using the calibrated conditional DFN models. This work proves the significance of conditioned DFN models in rock wedge stability analysis. Such models provide detailed information regarding rock wedge stability so that site monitoring and prevention plans can be conducted with higher efficiency.
