A Rapid Updating Method to Predict Grade Heterogeneity at Smaller Scales

Abstract

Measures of grade heterogeneity, or the spatial distribution of grades, depend on the scale of sampling. At the resource modelling scale, heterogeneity measures are limited to the scale of the data used to estimate the model. As denser sampling becomes available (e.g., from blast holes immediately prior to mining), it is, in principle, possible to provide measures of heterogeneity at smaller scales to allow selective mining of large resource blocks. However, this can only be done if the local resource model can be updated rapidly with the newly acquired data in time for selectivity decisions to be made (e.g., selective blasting and loading from a resource block). The economic value of quantifying small-scale grade heterogeneity is significant in terms of mining selectivity and recoverability. This study proposes an approach, based on the Kalman filter, for near real-time resource model downscaling and updating by integrating additional data from production blast holes. In this approach, the model assimilates newly acquired data and generates measures of small-scale grade heterogeneity to provide a basis on which better selective mining and loading decisions can be made. A synthetic dataset is used to demonstrate and validate the algorithm. The results show that the proposed algorithm is capable of updating a resource model in near real time and identifying 68% of the small-scale grade variability within a mining block.