Predicting stope performance at an octree resolution using multivariate models
DOI:
https://doi.org/10.17159/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 because of the high extraction rate and low costs At mining sites, stope performance is evaluated by calculating stope overbreak (OB; rock mined outside of the designed volume) using the Stability Chart. Limitations of the Stability Chart in regards to the precision of the predictions, non-consideration to factors such as the influence of blasting, and the exclusion of underbreak (UB; rock in the designed volume left behind) has led to non-optimal design. The computational capabilities of today’s computers have increased the amount of data that is being collected and the power of models that are being built. This paper presents a step towards a new stope design approach, where stope OB and UB is measured and georeferenced at an approximately cubic meter resolution (octrees) and predicted using statistical multivariate models (partial least square [PLS], linear discriminant analysis [LDA] and Random Forest). Results showed that OB and UB location along the design surface as well as their magnitude is predicted with good precision using a Random Forest model. These predictions are used to build the expected geometry of the void. The resolution of the data and the use of multivariate analysis has enabled the prediction of the variation of stope performance along the design surface, going well beyond the simple qualitative per stope face prediction provided by a traditional Stability Chart approach.
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Copyright (c) 2024 Benoît McFadyen, Martin Grenon, Kyle Woodward, Yves Potvin
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