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

arXiv: https://arxiv.org/abs/2005.14427 · HTML: https://mlsanity.com/paper/2005.14427/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 2 of 2)

- [Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modelling and Automation](https://mlsanity.com/paper/2103.03923/) (2021)
- [Empirical observations on the effects of data transformation in machine learning classification of geological domains](https://mlsanity.com/paper/2106.05855/) (2021)

## Abstract (arXiv)

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.