Eef Meerschman

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.

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.