GIS-Based Slope Stability Analysis, Chuquicamata Open Pit Copper Mine, Chile

Abstract

The risk of slope failure in the Chuquicamata open-pit mine was analyzed using Geographic Information System (GIS) software and modeling techniques. Models incorporated various component layers at a relatively large map scale (1:5000): alteration, geotechnical unit, proximity to major faults (VIF), GSI (geological strength index), slope (from digital elevation model), proximity to watertable (difference grid between topography and modeled watertable), and composite structural density grid (VIF, smaller faults, and fracture frequency); not all layers were used in all models. Three modeling techniques were used: fuzzy logic, in which parameters in each component layer were ranked by mine geotechnical experts according to their influence in promoting slope failure, and two data-driven techniques, weights-of-evidence and logistic regression, in which statistical correlation of training points (known failures) with parameters were used to derive a relative probability of failure. Because most slope failures are controlled by structure, VIF and smaller faults were divided by orientation into subsets with dip direction parallel, opposite, and normal to slope aspect; these orientations promote circular and planar, toppling, and wedge-type failures, respectively. Density grids of these subsets show high-risk areas for individual failure types.

The models demonstrate sensitivity of the analysis to (1) selection of component layers, (2) selection of training points, (3) classification and ranking of categorical parameters, and (4) data problems in certain layers. Predicted high-risk zones in the final models show a high degree of correspondence with recent, post-model failures. Such models can be used to anticipate future pit design concerns. The results presented here illustrate how vast amounts of data, in multiple geo-referenced layers, can be analyzed and modeled using GIS techniques for predictive studies at relatively large map scales. Such modeling techniques could provide a powerful tool for predictive modeling in a vast array of large-map-scale applications requiring similar data integration and evaluation.