Backpropagation-based inference for spatial interpolation to estimate the blastability index in an open pit mine

The blastability index (BI) is a measure that indicates the resistance of rock to fragmentation when blasting. With novel technologies, miners are now able to collect and calculate BI at different depths while drilling. In this research, we propose an approach to estimate the BI at multiple depths for new areas using only spatial locations and observed BI measurements of previously drilled holes. Spatial interpolation techniques are investigated. This study introduces a novel treatment for Gaussian Processes (GPs) and Inverse Distance Weighting (IDW). Variography is leveraged to ensure an appropriate fit between the data and the spatial component. The parameters controlling anisotropy are constrained to intervals chosen to reflect the observed anisotropy. Gradient descent with back-propagation is used for optimization. The proposed approach improves the performance of GP and IDW at predicting BI. The similarities between the IDW variant proposed and a single-layer neural network are discussed. •Innovates BI prediction using spatial and observed measurements.•Improves Gaussian Process (GP) and IDW methods for BI prediction.•Constrains parameters for anisotropy intervals in spatial models.•Uses backpropagation to optimize GP and IDW for better accuracy.•Introduces robust GBM-IDW-LHOCV for precise, regularized BI predictions.