Bayesian shape modelling of cross-sectional geological data

TL;DR

This paper introduces a Bayesian approach for statistically analyzing cross-sectional geological shapes, providing a more rigorous framework than previous simplistic classifications, with potential applications beyond geology.

Contribution

It derives the integrated likelihood for shape data given class parameters, enabling coherent Bayesian shape modeling in geological applications.

Findings

First step towards statistical shape analysis in geology

Derivation of integrated likelihood for shape data

Framework applicable to other shape classification problems

Abstract

Shape information is of great importance in many applications. For example, the oil-bearing capacity of sand bodies, the subterranean remnants of ancient rivers, is related to their cross-sectional shapes. The analysis of these shapes is therefore of some interest, but current classifications are simplistic and ad hoc. In this paper, we describe the first steps towards a coherent statistical analysis of these shapes by deriving the integrated likelihood for data shapes given class parameters. The result is of interest beyond this particular application.