ABSTRACT
In this paper, data from an open pit mine in Western Africa were used to generate spatially constrained large-scale DFN models. Structural data from geotechnical boreholes and borehole tele-viewers, drilled and logged in the pit area, were used to develop stochastic 3D block models of volumetric fracture intensity (P32) using Sequential Gaussian Simulation. A geocellular 3D DFN model with finite volume of cellular grid elements were then created and spatially constrained based on the 3D block models of P32. This approach ensures that the fracture centers in the DFN generation are under the influence of cellular P32 values at corresponding locations, estimated based on the 3D block model of volumetric fracture intensity. The joint orientations were bootstrapped based on the joint orientations observed along the drillholes. The geocellular DFN models were further calibrated using joint trace mapping data, collected across the pit area using a drone-based aerial photogrammetry. The resulting DFN models are expected to accurately reflect the spatial variation of fracture geometry and intensity along the pit area. Finally, 2D cross sections of the generated DFN models were incorporated into 2D finite element models of the pit slope for a stochastic slope stability analysis. The results of the stochastic modeling were compared to a simplistic approach assuming an average rock mass structural condition.
