  This paper illustrates an application of machine learning (ML) within a
complex system that performs grade estimation. In surface mining, assay
measurements taken from production drilling often provide rich information that
allows initially inaccurate surfaces created using sparse exploration data to
be revised and subsequently improved. Recently, a Bayesian warping technique
has been proposed to reshape modeled surfaces based on geochemical and spatial
constraints imposed by newly acquired blasthole data. This paper focuses on
incorporating machine learning in this warping framework to make the likelihood
computation generalizable. The technique works by adjusting the position of
vertices on the surface to maximize the integrity of modeled geological
boundaries with respect to sparse geochemical observations. Its foundation is
laid by a Bayesian derivation in which the geological domain likelihood given
the chemistry, p(g|c), plays a similar role to p(y(c)|g). This observation
allows a manually calibrated process centered around the latter to be automated
since ML techniques may be used to estimate the former in a data-driven way.
Machine learning performance is evaluated for gradient boosting, neural
network, random forest and other classifiers in a binary and multi-class
context using precision and recall rates. Once ML likelihood estimators are
integrated in the surface warping framework, surface shaping performance is
evaluated using unseen data by examining the categorical distribution of test
samples located above and below the warped surface. Large-scale validation
experiments are performed to assess the overall efficacy of ML assisted surface
warping as a fully integrated component within an ore grade estimation system
where the posterior mean is obtained via Gaussian Process inference with a
Matern 3/2 kernel.
