Lyell Collection
Geological Society, London, Special Publications
Lyell Centre  |   Lyell Collection  |   Subscriptions   |   Geological Society  |   Email alerts  |   Online bookshop  |   Help

Keywords:
Author:
Advanced search>>
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Request Permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Stephenson, J.
Right arrow Articles by Holmes, C. C.
Right arrow Search for Related Content
GeoRef
Right arrow GeoRef Citation
Geological Society, London, Special Publications; 2004; v. 239; p. 195-209;
DOI: 10.1144/GSL.SP.2004.239.01.13
© 2004 Geological Society of London

Applications

Beyond kriging: dealing with discontinuous spatial data fields using adaptive prior information and Bayesian partition modelling

John Stephenson1, K. Gallagher1 & C. C. Holmes2

1 Department of Earth Science and Engineering, Imperial College London, South Kensington, London SW7 2AS, UK john.stephenson{at}imperial.ac.uk
2 Department of Mathematics, Imperial College London, South Kensington, London SW7 2AS, UK

The technique of kriging is widely known to be limited by its assumption of stationarity, and performs poorly when the data involve localized effects such as discontinuities or nonlinear trends. A Bayesian partition model (BPM) is compared with results from ordinary kriging for various synthetic discontinuous 1-D functions, as well as for 1986 precipitation data from Switzerland. This latter dataset has been analysed during a comparison of spatial interpolation techniques, and has been interpreted as a stationary distribution and one thus suited to kriging. The results demonstrate that the BPM outperformed kriging in all of the datasets compared (when tested for prediction accuracy at a number of validation points), with improvements by a factor of up to 6 for the synthetic functions.





This article has been cited by other articles:


Home page
 Geological Society, London, Special PublicationsHome page
R. Wood and A. Curtis
Geological prior information and its applications to geoscientific problems
Geological Society, London, Special Publications, 2004; 239: 1 - 14.
[Abstract] [PDF]