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This paper presents a Bayesian framework for manipulating mesh surfaces withthe aim of improving the positional integrity of the geological boundaries thatthey seek to represent. The assumption is that these surfaces, createdinitially using sparse data, capture the global trend and provide a reasonableapproximation of the stratigraphic, mineralisation and other types ofboundaries for mining exploration, but they are locally inaccurate at scalestypically required for grade estimation. The proposed methodology makes localspatial corrections automatically to maximise the agreement between themodelled surfaces and observed samples. Where possible, vertices on a meshsurface are moved to provide a clear delineation, for instance, between ore andwaste material across the boundary based on spatial and compositional analysis;using assay measurements collected from densely spaced, geo-registered blastholes. The maximum a posteriori (MAP) solution ultimately considers thechemistry observation likelihood in a given domain. Furthermore, it is guidedby an apriori spatial structure which embeds geological domain knowledge anddetermines the likelihood of a displacement estimate. The results demonstratethat increasing surface fidelity can significantly improve grade estimationperformance based on large-scale model validation.

Bayesian Surface Warping Approach For Rectifying Geological Boundaries Using Displacement Likelihood And Evidence From Geochemical Assays