Modelling the Distribution and Quality of Sand and Gravel Resources in 3D: a Case Study in the Thames Basin, UK

  • K. MeeEmail author
  • B. P. Marchant
  • J. M. Mankelow
  • T. P. Bide


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.


3D model Mineral resource model Additive log-ratio transform Linear model of coregionalization 



This paper is published with the permission of the Executive Director of the British Geological Survey (NERC). It was funded by BGS National Capability funding from NERC. Contains Ordnance Survey Data © Crown copyright and database rights [2018]. Ordnance Survey Licence no. 100021290. The authors would like to thank Dr. David Malone, Andy Kingdon, John Williams and another anonymous reviewer for constructive reviews which have greatly improved this manuscript. Maps were created using ArcGIS® software by Esri. ArcGIS® and ArcMap™ are the intellectual property of Esri and are used herein under license. Copyright © Esri. All rights reserved. For more information about Esri® software, please visit


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.British Geological Survey, Environmental Science CentreNottinghamUK

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