|Multivariate geostatistics for the predictive modelling of the surficial sand distribution in shelf seas|Verfaillie, E.; Van Lancker, V.; Van Meirvenne, M. (2006). Multivariate geostatistics for the predictive modelling of the surficial sand distribution in shelf seas. Cont. Shelf Res. 26(19): 2454-2468. dx.doi.org/10.1016/j.csr.2006.07.028
In: Continental Shelf Research. Pergamon Press: Oxford. ISSN 0278-4343, more
|Also published as |
- Verfaillie, E.; Van Lancker, V.; Van Meirvenne, M. (2007). Multivariate geostatistics for the predictive modelling of the surficial sand distribution in shelf seas, in: (2007). VLIZ Coll. Rep. 35-36(2005-2006). VLIZ Collected Reprints: Marine and Coastal Research in Flanders, 35-36: pp. Chapter 67, more
- Verfaillie, E.; Van Lancker, V.; Van Meirvenne, M. (2008). Multivariate geostatistics for the predictive modelling of the surficial sand distribution in shelf seas, in: Verfaillie, E. (2008). Ontwikkeling en validering van een ruimtelijke verspreidingsmodellen van mariene habitats, ter ondersteuning van het ecologisch waarderen van de zeebodem = Development and validation of spatial distribution models of marine habitats, in support of the ecological valuation of the seabed. pp. 51-72, more
Bathymetry; Grain size; Mapping; ANE, Belgium, Belgian Continental Shelf (BCS) [gazetteer]; Marine
Multivariate geostatistics; Median grain-size; Bathymetry; Habitat mapping; Resource maps; Belgian continental shelf
Multivariate geostatistics have been used to obtain a detailed and high-quality map of the median grain-size distribution of the sand fraction at the Belgian Continental Shelf. Sandbanks and swales are the dominant geomorphological features and impose a high-spatial seafloor variability. Interpolation over complex seafloors is difficult and as such various models were investigated. In this paper, linear regression and ordinary kriging (OK) were used and compared with kriging with an external drift (KED) that makes use of secondary information to assist in the interpolation. KED proved to be the best technique since a linear correlation was found between the median grain-size and the bathymetry. The resulting map is more realistic and separates clearly the sediment distribution over the sandbanks from the swales. Both techniques were also compared with a simple linear regression of the median grain-size against the bathymetry. An independent validation showed that the linear regression yielded the largest average prediction error (almost twice as large as with KED).
Unlike most static sedimentological maps, our approach allows for defining grain-size classes that can be adapted according to the needs of various applications. These relate mainly to the mapping of soft substrata habitats and of the most suitable aggregates for extraction. This information is highly valuable in a marine spatial planning context.
- Silt-clay map (UG_RCMG_sicl), more