BGS Predictive Seabed Sediments (UK)

The BGS Predictive Seabed_Sediments (UK) data provide digital maps of seabed sediment (SBS) composition across the UK Continental Shelf (UKCS). These sediments are those that are present at seabed, the interface between the geological substrate below, and the water column above.  The physical properties and grain size (or composition) of these sediments depends on a variety of factors, including: the nature of the underlying geology (e.g. glacial vs. unconsolidated marine deposits), the hydrodynamic environment, terrigenous sediment input (e.g. fluvial), biogeochemical processes operating at seabed, and the physiography and geomorphology of the seabed environment. Seabed sediments are commonly unconsolidated, and as such are potentially transitory and mobile over variable timescales.

These predictive maps are generated using a machine learning algorithm known as a Distributional Random Forest (DRF).  The input data consists of more than 38,000 seabed sediment samples (collated from various sources) with measurements of the proportion of mud, sand and gravel from locations across the study area. Model inputs also include numerous spatially-continuous covariates likely to have a degree of influence on the spatial distribution of seabed sediments. Covariates included bathymetry data, morphometric derivatives (at multiple spatial scales), as well as hydrodynamic data layers (currents and tidal).

The predictive maps include one classified SBS map (Folk sediment class; vector), and three percentage-sediment maps giving the proportions of gravel, sand, and mud (rasters). Further details on the development of these map products can be found with the User Guide.

These predictive SBS maps are intended as an update to, and can be contrasted with previous BGS Seabed sediments mapping.