  This paper presents a Bayesian framework for manipulating mesh surfaces with
the aim of improving the positional integrity of the geological boundaries that
they seek to represent. The assumption is that these surfaces, created
initially using sparse data, capture the global trend and provide a reasonable
approximation of the stratigraphic, mineralisation and other types of
boundaries for mining exploration, but they are locally inaccurate at scales
typically required for grade estimation. The proposed methodology makes local
spatial corrections automatically to maximise the agreement between the
modelled surfaces and observed samples. Where possible, vertices on a mesh
surface are moved to provide a clear delineation, for instance, between ore and
waste material across the boundary based on spatial and compositional analysis;
using assay measurements collected from densely spaced, geo-registered blast
holes. The maximum a posteriori (MAP) solution ultimately considers the
chemistry observation likelihood in a given domain. Furthermore, it is guided
by an apriori spatial structure which embeds geological domain knowledge and
determines the likelihood of a displacement estimate. The results demonstrate
that increasing surface fidelity can significantly improve grade estimation
performance based on large-scale model validation.
