Modelling the distribution and quality of sand and gravel resources in 3D: a case study in the Thames Basin, UK

Mee, K.; Marchant, B.P.; Mankelow, J.M.; Bide, T.P.. 2019 Modelling the distribution and quality of sand and gravel resources in 3D: a case study in the Thames Basin, UK. Environmental Modeling & Assessment, 24 (5). 585-603.

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Three-dimensional (3D) models are often utilised to assess the presence of sand and gravel deposits. Expanding these models to provide a better indication of the suitability of the deposit as aggregate for use in construction would be advantageous. This, however, leads to statistical challenges. To be effective, models must be able to reflect the interdependencies between different criteria (e.g. depth to deposit, thickness of deposit, ratio of mineral to waste, proportion of ‘fines’) as well as the inherent uncertainty introduced because models are derived from a limited set of boreholes in a study region. Using legacy borehole data collected during a systematic survey of sand and gravel deposits in the UK, we have developed a 3D model for a 2400 km2 region close to Reading, southern England. In developing the model, we have reassessed the borehole grading data to reflect modern extraction criteria and explored the most suitable statistical modelling technique. The additive log-ratio transform and the linear model of coregionalization have been applied, techniques that have been previously used to map soil texture classes in two dimensions, to assess the quality of sand and gravel deposits in the area. The application of these statistical techniques leads to a model which can be used to generate thousands of plausible realisations of the deposit which fully reflect the extent of model uncertainty. The approach offers potential to improve regional-scale mineral planning by providing an enhanced understanding of sand and gravel deposits and the extent to which they meet current extraction criteria.

Item Type: Publication - Article
Digital Object Identifier (DOI):
ISSN: 1420-2026
Date made live: 17 Apr 2019 15:00 +0 (UTC)

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