Spatial prediction of seabed sediment texture classes by cokriging from a legacy database of point observations

This paper illustrates the potential for statistical mapping of seabed sediment texture classes. It reports the analysis of legacy data on the composition of seabed sediment samples from the UK Continental Shelf with respect to three particle size classes (sand, mud, gravel). After appropriate transformation for compositional variables the spatial variation of the sediment particle size classes was modelled geostatistically using robust variogram estimators to produce a validated linear model of coregionalization. This was then used to predict the composition of seabed sediments at the nodes of a fine grid. The predictions were back-transformed to the original scales of measurement by a Monte Carlo integration over the prediction distribution on the transformed scale. This approach allowed the probability to be computed for each class in a classification of seabed sediment texture, at each node on the grid. The probability of each class, and derived information such as the class of maximum probability could therefore be mapped. Predictions were validated at a set of 2000 randomly sampled locations. The class of maximum probability corresponded to the observed class with a frequency of 0.7, and the uncertainty of this prediction was shown to depend on the absolute probability of the class of maximum probability. Other tests showed that this geostatistical approach gives reliable predictions with meaningful uncertainty measures. This provides a basis for rapid mapping of seabed sediment texture to classes with sound quantification of the uncertainty. Remapping to revised class definitions can also be done rapidly, which will be of particular value in habitat mapping where the seabed geology is an important factor in biotope modelling.

► We model transformed particle size distributions from the UK continental shelf.
► We then use cokriging to map particle size distribution and sediment texture classes.
► Uncertainty measures for the predicted class are computed and validated.