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Investigation of archaeological monuments through coring is a challenging task regarding the understanding of the composition, stratigraphy, and inner structure of monuments. We propose a method of statistical classification and identification of construction materials of earthworks sampled by coring technique, which was applied for the semi-circular rampart in the Viking settlement Hedeby.

The physicochemical properties of 139 samples from two cores through the top of the rampart and from one core through the buried ditch at the outer footslope were determined: weight percentages of gravel, artefacts, bones and charcoal, loss on ignition, magnetic susceptibility, grain size distribution, and element concentrations. After statistical pre-treatment of data (normalization, correlation, standardization), principal component analysis and cluster analysis were applied to distinguish and group the materials according to their physicochemical properties. The resulting clusters were used as a basis for material classification. Some clusters were mildly transformed according to the morphological properties of material.

As a result, 20 groups of materials were distinguished. We could conclude that the rampart consists of material originating from soil horizons, cultural deposits, Pleistocene materials, and mixtures of these materials. These materials were used in different proportions along the investigated transect. It was also revealed that the volume of material excavated from the ditches was not sufficient for the rampart. Due to a large portion of soil horizons in the rampart, it could be concluded that topsoil horizons must have been extracted from large areas around the rampart.

The presented method for statistical classification of material facilitated and objectified the identification of the genesis and possible sources of the materials. Application of this method on additional transects, best combined with geophysical surveys, would help reconstructing the large-scale anthropogenic landscape transformation in Hedeby.

EAA 2023: Abstract

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