2.4.1. Sedimentological Data

Unsupervised classification of the sediment properties examined was used to verify stratigraphic delineation conducted during fieldwork. The most recent work of Schmidt et al. [43] shows that hierarchical clustering of element concentrations may be used to classify sediment data for stratigraphic delineation and interpretation. Building upon their work, we applied cluster analysis of Manhattan distances performed with Ward's method (e.g., [64]). Additionally we examined the benefit of depth-constrained clustering [65] using CONISS [66]. The number of clusters/groups is based on the layers identified in Figure 2c: Four main layers (A, B, C, D) and two additional layers, representing the sediment parts within the stone layers. The total number of six clusters was increased to 10 clusters for depth-constrained clustering, now including transitional layers between the main layers. Both clustering approaches were conducted for (i) the pre-processed element concentrations (p-ED-XRF), the pre-processed spectra of the (ii) ground and (iii) unground sediment samples, and (iv) the median pixel values of the RGB and (v) multispectral images, extracted from the sampling locations.
