Bayesian shape modelling of cross-sectional geological data

Tsiftsi, Thomai; Jermyn, Ian; Einbeck, Jochen

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.

Citation

Tsiftsi, T., Jermyn, I., & Einbeck, J. (2014, July). Bayesian shape modelling of cross-sectional geological data. Presented at 29th International Workshop on Statistical Modelling, Göttingen

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 29th International Workshop on Statistical Modelling
• Publication Date: Jul 18, 2014
• Deposit Date: Oct 1, 2014
• Publicly Available Date: Oct 6, 2014
• Volume: 2
• Pages: 161-164
• Book Title: 29th International Workshop on Statistical Modelling, 14-18 July 2014, Göttingen, Germany ; proceedings.
• Keywords: Shape analysis, Classification, Estimation, EM algorithm.
• Public URL: https://durham-repository.worktribe.com/output/1154962
• Publisher URL: http://www.statmod.org/workshops_archive_proceedings_2014.htm
• Related Public URLs: http://www.statmod.org/workshops_archive_proceedings_2014.htm