Detailed knowledge of the content and geometrical variation of ore grade is essential in mining operations for production planning and economic analysis. Common ore grade specification methods, sampling and analysis are costly and time consuming. Measurement While Drilling (MWD) technique can directly extract grade information from the drilling process increasing data resolution and reducing cost.

This study introduces a supervised feature selection method based on the Hilbert-Schmidt independence criterion to increase the accuracy of the results and decrease processing time. Potential of the method for recognizing the most effective and non-repetitive dimensions of input data has also been investigated. By exploiting the lower dimension data, a classification model is developed to map the parameter values to ore grade levels.

Evaluation of the model using MWD data from LKAB’s Leveäniemi mine proved the effectiveness of the proposed feature selection and classification method.