Abstract
The economic viability of a mining project depends on its efficient exploration, which requires a prediction of worthwhile ore in a mine deposit. In this work, we apply the so-called LASSO methodology to estimate mineral concentration within unexplored areas. Our methodology outperforms traditional techniques not only in terms of logical consistency, but potentially also in costs reduction. Our approach is illustrated by a full source code listing and a detailed discussion of the advantages and limitations of our approach.
Highlights
Any mining project begins with the exploration stage which includes estimation of the expected economic returns, which can be made after significant mineralization has been encountered
Thereby, the focus is on making predictions for unexplored areas based on limited drilling data and using this to estimate anticipated costs and potential revenues
Prediction of grade variability is often viewed as an interpolation methodology, which has been criticized for a number of restrictive assumptions [5,6,7]
Summary
Any mining project begins with the exploration stage which includes estimation of the expected economic returns, which can be made after significant mineralization has been encountered. We utilize recent developments in high-dimensional statistics which provide a control on prediction accuracy by the reduction of the so-called “generalization error”. The reduction of generalization error is achieved using appropriate penalization and regularization techniques, which are usually combined with some parameter selection method In our approach, such parameter selection is based on in-sample estimation of predictive performance, effected by the so-called “cross-validation”. We demonstrate how drilling data can be combined with penalized regression to establish a statistical model focused on predictive performance. The results of this approach are compared to a traditional technique
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