Bayesian Surface Warping Approach for Rectifying Geological Boundaries Using Displacement Likelihood and Evidence from Geochemical Assays

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