Taxonomic identification of whale bones found during archaeological excavations is problematic due to their typically fragmented state. This difficulty limits understanding of both the past spatio-temporal distributions of whale populations and of possible early whaling activities. To overcome this challenge, we performed zooarchaeology by mass spectrometry on an unprecedented 719 archaeological and palaeontological specimens of probable whale bone from Atlantic European contexts, predominantly dating from ca 3500 BCE to the eighteenth century CE. The results show high numbers of Balaenidae (many probably North Atlantic right whale ( Eubalaena glacialis )) and grey whale ( Eschrichtius robustus ) specimens, two taxa no longer present in the eastern North Atlantic. This discovery matches expectations regarding the past utilization of North Atlantic right whales, but was unanticipated for grey whales, which have hitherto rarely been identified in the European zooarchaeological record. Many of these specimens derive from contexts associated with mediaeval cultures frequently linked to whaling: the Basques, northern Spaniards, Normans, Flemish, Frisians, Anglo-Saxons and Scandinavians. This association raises the likelihood that early whaling impacted these taxa, contributing to their extirpation and extinction. Much lower numbers of other large cetacean taxa were identified, suggesting that what are now the most depleted whales were once those most frequently used.

Hyperbolic diffractions in Ground Penetrating Radar (GPR) data are caused by a variety of subsurface objects such as pipes, stones, or archaeological artifacts. Supplementary to their location, the propagation velocity of electromagnetic waves in the subsurface can be derived. In recent years, it was shown that deep learning tools can automatically detect hyperbola in radargrams using data measured over urban infrastructure, which are relatively clear. In contrast, in this study, we used an archaeological dataset with diverse underground structures. In the first step we used the deep learning network RetinaNet to detect hyperbola automatically and achieved an average precision of 0.58. In the next step, 10 different approaches for hyperbola fitting and thus velocity determination were applied. The derived information was validated with manually determined velocities and apex points. It was shown that hyperbola extraction by using a threshold and a column connection clustering (C3) algorithm followed by simple hyperbola fitting is the best method, which had a mean velocity error of 0.021 m/ns compared to manual determination. The average 1D velocity-depth distribution derived in 10 ns intervals was in shape comparable to the manually determined one, but had a systematic shift of about 0.01 m/ns towards higher velocities.

Hyperbolae in radargrams are caused by a variety of small subsurface objects. The analysis of their curvature enables the determination of propagation velocity in the subsurface, which is important for exact time-to-depth conversion and migration and also yields information on the water content of the soil. Using deep learning methods and fitting (DLF) algorithms, it is possible to automatically detect and analyze large numbers of hyperbola in 3D Ground-Penetrating Radar (GPR) datasets. As a result, a 3D velocity model can be established. Combining the hyperbola locations and the 3D velocity model with reflection depth sections and timeslices leads to improved archaeological interpretation due to (1) correct time-to-depth conversion through migration with the 3D velocity model, (2) creation of depthslices following the topography, (3) evaluation of the spatial distribution of hyperbolae, and (4) derivation of a 3D water content model of the site. In an exemplary study, we applied DLF to a 3D GPR dataset from the multi-phased (2nd to 12th century CE) archaeological site of Goting on the island of Föhr, Northern Germany. Using RetinaNet, we detected 38,490 hyperbolae in an area of 1.76 ha and created a 3D velocity model. The velocities ranged from approximately 0.12 m/ns at the surface to 0.07 m/ns at approx. 3 m depth in the vertical direction; in the lateral direction, the maximum velocity variation was ±0.048 m/ns. The 2D-migrated radargrams and subsequently created depthslices revealed the remains of a longhouse, which was not known beforehand and had not been visible in the unmigrated timeslices. We found hyperbola apex points aligned along linear strong reflections. They can be interpreted as stones contained in ditch fills. The hyperbola points help to differentiate between ditches and processing artifacts that have a similar appearance as the ditches in time-/depthslices. From the derived 3D water content model, we could identify the thickness of the archaeologically relevant layer across the whole site. The layer contains a lot of humus and has a high water retention capability, leading to a higher water content compared to the underlying glacial moraine sand, which is well-drained.

Sylt was published in Band 35 Speckstein - Zwiebel. (Nachträge und Ergänzungen) on page 50.