a b s t r a c tThe 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.

Understanding palaeotopographical variability forms the basis for understanding prehistoric societies.Alluvial and lacustrine environments, in particular, are key areas with both a high archaeological and palaeoecological potential. However, the often deep stratification of these sites, the high water table and the complex sedimentological variations can hamper a detailed reconstruction of the spatial relationship between prehistoric settlement and their environment. Combining different remote and proximal sensing techniques and coring data, can offer detailed insight into such landscapes. More specifically, the integration of mobile geophysical methods allows the collection of unprecedented continuous information on large-scale palaeolandscape variability. In this study we present a combined approach in order to map and model prehistoric landscapes and river systems in and around a Late Glacial palaeolake in north-western Belgium. Based on filtered and unfiltered digital elevation models, a survey area of 60 ha was selected, in which detailed mobile multi-receiver electromagnetic induction survey was conducted. The results allowed for the delineation of palaeochannels in the area and enabled modelling the depth of these features in the survey area, providing insight into their flow characteristics.14 C sampling enabled the dating of the evolving river system to the transition between the Late Glacial and the Early Holocene. Through additional coring, this river system could be traced further through the palaeolake area. Based on these results a detailed reconstruction was made of the palaeotopography that harboured the Final Palaeolithic andEarly Mesolithic occupation of the study site.

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Multiple apparent electrical conductivity (EC a ) measurements with an electromagnetic induction (EMI) sensor frequently reveal analogue patterns caused by conductive features in the soil. A procedure was proposed to highlight different archaeological anomalies based on combinations of the simultaneous EC a measurements with the DUALEM-21S instrument. After selection of a 3.5 ha study site, 0.79 ha has been recorded by archaeological excavation. Since the majority of the archaeological features were found between the plough layer and 1.0 m below the soil surface, a set of four equations were developed to model the EC within that predefined depth interval. This set of four equations employed the four depth response curves specific to the four DUALEM-21S coil configurations. The modelled conductivity between 0.5 and 1.0 m (EC Ã 2 ) showed a larger variability across the archaeological features than the raw EC data. To quantify the added value of this modelled conductivity, EC Ã 2 and measured EC a were compared with the rasterized map of the archaeological traces. Finally, the EC Ã 2 map proved to be better able to distinguish between the archaeological features and the 'empty' background. This technique allowed the highlighting of vague anomalies in the simultaneous DUALEM-21S EC a measurements. m, mean in mS m À1 after conversion to a reference temperature of 25 C; CV, coefficient of variation in %; RD, relative difference in %.

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