The Chalk revolution – the role of geological mapping for engineering and hazard assessment

Since the last Chalk symposium in 1989 in Brighton, our understanding of the UK Chalk has undergone a revolution, not just in terms of the stratigraphical and engineering properties of this enigmatic material, but also in its spatial distribution and variability in 2D and 3D space from geological mapping. The old tripartite Chalk stratigraphy has been replaced with a more detailed stratigraphical scheme with up to nine Formations. The increased stratigraphical precision this brings has revealed far more geological structure and facies variability across the outcrop than previously recognized. Most of the major basin boundary faults, including the Hog’s Back, Mere and Pewsey Faults are now known to cut the Chalk sequence. Geological mapping has also provided a wealth of information on associated superficial and mass movement deposits, karst features and hydrogeology. The role of the engineering geology community in providing data has been a key part of this revolution. High precision site-specific information from ground investigations including the identification of key stratigraphical marker beds in boreholes, quarry and coastal sections is critical in characterizing the Chalk succession. Such point-specific data combined with spatially extensive data derived from geological mapping data enables thickness and facies trends to be identified and allows the prediction of ground conditions over wide areas. This paper outlines how our understanding of the facies variability and structure of the Chalk can be improved using high resolution geological mapping, and how this can benefit the engineering community using specific examples. Advances in technology means that the Chalk is increasingly being visualized in three dimensions through the production of 3D geological models that can be updated dynamically as new site investigation data comes in. These models can be used as a geotechnical risk management tool, promoting a cycle of risk reduction. The accuracy and resolution of these models depends on the quantity, quality and spatial distribution of 3D data, particularly the availability of accessible, high quality, well described borehole logs identifying key stratigraphical markers.