Property attribution of 3D geological models in the Thames Gateway, London : new ways of visualising geoscientific information
Royse, Katherine; Rutter, Helen; Entwisle, David. 2009 Property attribution of 3D geological models in the Thames Gateway, London : new ways of visualising geoscientific information. Bulletin of Engineering Geology and the Environment, 68 (1). 1-16. https://doi.org/10.1007/s10064-008-0171-0
Before downloading, please read NORA policies.
|
Text
Royse_et_al2008.pdf Download (1MB) | Preview |
Abstract/Summary
Rapid developments in information technology and the increasing collection and digitisation of geological data by the British Geological Survey now allow geoscientists to produce meaningful 3D spatial models of the shallow subsurface in many urban areas. Using this new technology, it is possible to model and predict not only the type of rocks in the shallow subsurface, but also their engineering properties (rock strength, shrink-swell characteristics and compressibility) and hydrogeological properties (permeability, porosity, thickness of the unsaturated zone or the likelihood of perched water tables) by attribution of the 3D model with geological property data. This paper describes the hydrogeological, engineering and confidence (uncertainty) attribution of high resolution models of the Thames Gateway Development Zone (TGDZ) east of London UK and proposes a future in which site investigation sets out to test a pre-existing spatial model based on real data rather than a conceptual model.
Item Type: | Publication - Article |
---|---|
Digital Object Identifier (DOI): | https://doi.org/10.1007/s10064-008-0171-0 |
Programmes: | BGS Programmes 2008 > Land use and development |
ISSN: | 1435-9529 |
Additional Information. Not used in RCUK Gateway to Research.: | The original publication is available at www.springerlink.com |
NORA Subject Terms: | Earth Sciences |
Date made live: | 06 Nov 2008 14:10 +0 (UTC) |
URI: | https://nora.nerc.ac.uk/id/eprint/4824 |
Actions (login required)
View Item |
Document Downloads
Downloads for past 30 days
Downloads per month over past year