Predicting open stope performance at an octree resolution using multivariate models

Abstract

Open stoping has become a popular mining method in hard rock mines, not only due to the safety of the method as a non-entry approach, but also because of the high extraction rate and low costs. At mine sites, stope performance is evaluated by calculating stope overbreak using the stability chart. However, limitations of the stability chart regarding the precision of the predictions, non-consideration of factors such as the influence of blasting, and the exclusion of underbreak have led to non-optimal designs. The capabilities of today's computers have increased the amount of data being collected and the power of models being built. This article presents a step towards a new stope design approach where stope overbreak and underbreak are measured and georeferenced using octrees at an approximately cubic metre resolution and predicted using multivariate statistical models (partial least square, linear discriminant analysis, and random forest). Results show that overbreak and underbreak location along the design surface and their magnitude are predicted with good precision using a random forest model. These predictions are used to build the expected geometry of the open stope. The resolution of the data and the use of multivariate analysis has enabled the prediction of variation in stope performance along the design surface, going well beyond the simple qualitative per stope face prediction provided by a traditional stability chart approach. Keywords: stope design, stope reconciliation, overbreak, underbreak, multivariate, prediction, random forest.