AUTOMATED BIOSTRATIGRAPHIC CORRELATION OF PALYNOLOGICAL RECORDS ON THE BASIS OF SHAPES OF POLLEN CURVES AND EVALUATION OF NEXT-BEST SOLUTIONS

We present a fully automated method to correlate palynological records on basis of the shapes of individual pollen curves. To this end the p ollen curve between two successive samples, named ''object'', is descr ibed by the following characteristics: (1) the length of the interval between the samples, (2) the average pollen percentage, or score, of t he curve within the interval, and (3) the slope of the curve within th e interval. The latter two characteristics, after having been transfor med to an ordinal scale comprising three classes, are used to determin e the dissimilarity between the objects. Dissimilarities are expressed as cost values, and are calculated on the basis of a simple look-up t able. The lengths of the intervals are not incorporated in the dissimi larity measure but are used as weight factors instead. As we do not ch arge costs for compressing the stratigraphical columns, they can be sq ueezed and stretched to a high degree in order to find the best correl ation. The best correlation between two pollen curves is defined as th e correlation for which the costs of aligning all objects of both curv es sum to the minimal total costs. Our method is not only applicable t o the curves of a single pollen taxon but also allows the incorporatio n of the curves of several taxa into a single analysis. Finally, in ad dition to the optimal alignment, we determine next-best correlations a s well. In order to visualize the entire set of best and next-best cor relations, we introduce a so-called cost-landscape. The x- and y-axes of the cost-landscape correspond to the depth axes of the two pollen p rofiles, whereas the total costs of the alternative alignments are dep icted as elevation. The optimal correlation appears as a river in the cost-landscape, while aspects of the river bank reveal the Bt of alter native matchings. We use a Geographic Information System (GIS) to expl ore the cost-landscape. As an illustration of the methodology we apply our matching procedure to a palynological data-set that covers the Ho locene period in northern Europe. These data enable us to compare the matching outcome with other correlation techniques, like correlation b y zones and slotting. We discuss several possible improvements of the procedure.