Combining teaching and research: a BIP on geophysical and archaeological prospection of North Frisian medieval settlement patterns

We performed a research-oriented EU Erasmus+ Blended Intensive Program (BIP) with participants from four countries focused on North Frisian terp settlements from Roman Iron Age and medieval times. We show that the complex terp structure and environment can be efficiently prospected using combined magnetic and EMI mapping, and seismic and geoelectric profiling and drilling. We found evidence of multiple terp phases and a harbor at the Roman Iron Age terp of Tofting. In contrast, the medieval terp of Stolthusen is more simply constructed, probably uni-phase. The BIP proved to be a suitable tool for high-level hands-on education adding value to the research conducted in on-going projects

Advancing the spatial characterization of peat layers through probabilistic 3D modelling

The reconstruction of peat layers in pedo- and geological models on the landscape scale attracts increasing attention in both policy making processes and land development projects. Considering the importance of accurate volume, depth and thickness predictions, and their associated uncertainty, as well as the propagation of uncertainty in derived models, for instance, on greenhouse gas emissions, there is a clear incentive to further develop probabilistic modelling tools. In this case, a highly heterogeneous 1000 ha study area in the Flemish Scheldt river estuary at the North Sea in Belgium is chosen, where a redevelopment project to expand the Port of Antwerp is ongoing. Within the area, 80 interpreted boreholes collected over the past decades are used as both discrete and parameterized continuous input data, for which respectively the associated geological member and lithological units have been identified up to a maximum of 12 m below surface. We compare deterministic modelling of lithology as applied in an existing regional 3D subsurface model, with more advanced probabilistic geostatistical methods to characterize a buried peat layer. Both approaches are assessed through independent validation of predictions with parameter optimization, including the reconstruction of spatial variability. The measure of uncertainty provided by probabilistic methods is expected to enhance the quantitative spatial analysis on the occurrence of peat in the subsurface