Any model plot that you create interactively by adding plot-items and adjusting settings can be represented by an equivalent set of commands. This is useful should you want to include command-driven plotting in your modeling run.
This tutorial will show how to create and manipulate zone plot items for showing model attributes and results.
Introduction to Python scripting by reviewing key concepts and through demonstrations. Part 2 focuses on classes and objects plus lists and dictionaries.
The Fabian orebody is a non-daylighting iron orebody in the LKAB Malmberget Mine in northern Sweden. During 2010, a prognosis of the cave development in the Fabian area was developed, based on compilation and analysis of all available material. In March 2012, a new cave crater formed on the ground surface above the Fabian orebody, similar to what was predicted. The prognosis is compared with observations of the caving and the differences and implications quantified. A program for continued monitoring of mining-induced deformation in Malmberget is also described and a criterion for allowable mining-induced surface deformations is proposed.
Field monitoring programs (e.g., convergence measurements and stress measurements in the support system) play an important role in following the response of the ground and of the support system during and after excavation. They contribute to the adaptation of the excavation and support installation method and the prediction of the long-term behavior. In the context of the Lyon–Turin link project, an access gallery (SMP2) was excavated between 2003 and 2010, and a survey gallery (SMP4) has been excavated since 2017.
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.