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Bayesian shape modelling of cross-sectional geological data

Tsiftsi, Thomai; Jermyn, Ian; Einbeck, Jochen

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Authors

Thomai Tsiftsi

Ian Jermyn



Contributors

Kneib Thomas
Editor

Sobotka Fabian
Editor

Fahrenholz Jan
Editor

Irmer Henriette
Editor

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). Bayesian shape modelling of cross-sectional geological data. In K. Thomas, S. Fabian, F. Jan, & I. Henriette (Eds.), 29th International Workshop on Statistical Modelling, 14-18 July 2014, Göttingen, Germany ; proceedings (161-164)

Conference Name 29th International Workshop on Statistical Modelling
Conference Location Göttingen
Publication Date Jul 18, 2014
Deposit Date Oct 1, 2014
Publicly Available Date Mar 29, 2024
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

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