In this tutorial we will go over meshing, from the creation of a 2D mesh and how to import it to MINEDW, to the inclusion of topography, layers, and pinch-outs to different areas of interest in the model.
Introduction to Python scripting by reviewing key concepts and through demonstrations. Part 3 focuses on modules and packages, with a focus on NumPy and Matplotlib.
Introduction to Python scripting by reviewing key concepts and through demonstrations. Part 2 focuses on classes and objects plus lists and dictionaries.
A geochemical model was developed to predict future water quality of the Cove pit lake in support of site closure and regulatory permitting.
A major use of DFN models for industrial applications is to evaluate permeability and flow structure in hardrock aquifers from geological observations of fracture networks. The relationship between the statistical fracture density distributions and permeability has been extensively studied, but there has been little interest in the spatial structure of DFN models, which is generally assumed to be spatially random (i.e., Poisson). In this paper, we compare the predictions of Poisson DFNs to new DFN models where fractures result from a growth process defined by simplified kinematic rules for nucleation, growth, and fracture arrest.
With increasing depth, higher stress and more difficult mining. With increasing depth is there more ground surface effects or less?