The 3-D reconstruction of medieval wetland reclamation through electromagnetic induction survey

Studies of past human-landscape interactions rely upon the integration of archaeological, biological and geological information within their geographical context. However, detecting the often ephemeral traces of human activities at a landscape scale remains difficult with conventional archaeological field survey. Geophysical methods offer a solution by bridging the gap between point finds and the surrounding landscape, but these surveys often solely target archaeological features. Here we show how simultaneous mapping of multiple physical soil properties with a high resolution multi-receiver electromagnetic induction (EMI) survey permits a reconstruction of the three-dimensional layout and pedological setting of a medieval reclaimed landscape in Flanders (Belgium). Combined with limited and directed excavations, the results offer a unique insight into the way such marginal landscapes were reclaimed and occupied during the Middle Ages. This approach provides a robust foundation for unravelling complex historical landscapes and will enhance our understanding of past human-landscape interactions

A geostatistical two-phase sampling strategy to map soil heavy metal concentrations in a former war zone

In a recent paper it was concluded that an enrichment of copper (Cu) content by 6 mg kg-1 dry mass in the soil around Ypres (Belgium) was a legacy of shelling during the First World War. This conclusion was based on a regional database of the entire province of West-Flanders (3144 km2) containing a limited number of soil samples in the war zone. Shells partly consisted of an alloy of Cu and zinc (Zn), and shrapnel balls were made out of lead (Pb). We expanded the database with a two-phase sampling design, each of 100 samples, in the war zone surrounding Ypres (640 km2) to (i) increase the detail of the inventory and (ii) expand the database to include Cu, Pb and Zn. This article focuses on the geostatistical selection of additional sampling locations. As the enrichment was spatially continuous and our aim was to map accurately over the range of values, rather than to delineate the enriched area, conventional selection criteria based on the probability of exceeding a critical threshold were not suitable. Therefore the sampling locations were optimized using a combination of selection criteria based on the kriging variance, the conditional variance and the conditional coefficient of variation obtained with sequential Gaussian simulation. A jackknife validation with 102 independent observations indicated the improvement after each phase. Additionally, the local uncertainty maps tended to show reduced values and a more homogenous pattern as additional samples were added. In an overview of the final prediction maps for Cu, Pb and Zn it is clear that those for Cu and Pb reflect the position of the main front line

A practical guide to performing multiple-point statistical simulations with the Direct Sampling algorithm

The Direct Sampling (DS) algorithm is a recently developed multiple-point statistical simulation technique. It directly scans the training image (TI) for a given data event instead of storing the training probability values in a catalogue prior to simulation. By using distances between the given data events and the TI patterns, DS allows to simulate categorical, continuous and multivariate problems. Benefiting from the wide spectrum of potential applications of DS, requires understanding of the user-defined input parameters. Therefore, we list the most important parameters and assess their impact on the generated simulations. Real case TIs are used, including an image of ice-wedge polygons, a marble slice and snow crystals, all three as continuous and categorical images. We also use a 3D categorical TI representing a block of concrete to demonstrate the capacity of DS to generate 3D simulations. First, a quantitative sensitivity analysis is conducted on the three parameters balancing simulation quality and CPU time: the acceptance threshold t, the fraction of TI to scan f and the number of neighbors n. Next to a visual inspection of the generated simulations, the performance is analyzed in terms of speed of calculation and quality of pattern reproduction. Whereas decreasing the CPU time by influencing t and n is at the expense of simulation quality, reducing the scanned fraction of the TI allows substantial computational gains without degrading the quality as long as the TI contains enough reproducible patterns. We also illustrate the quality improvement resulting from post-processing and the potential of DS to simulate bivariate problems and to honor conditioning data. We report a comprehensive guide to performing multiple-point statistical simulations with the DS algorithm and provide recommendations on how to set the input parameters appropriately