Bayesian shape modelling of cross-sectional geological data
This work addresses the need for better shape analysis in geology, particularly for applications like assessing oil-bearing capacity, but it is incremental as it describes only the first steps.
The paper tackled the problem of simplistic and ad hoc classification of cross-sectional geological shapes, such as sand bodies, by deriving the integrated likelihood for data shapes given class parameters as a step towards coherent statistical analysis.
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.