Archaeological Prospection最新文献

Tumuli, ancient burial mounds, stand as intriguing archaeological features, offering valuable insights into past cultures and burial practices. This paper explores the significance of tumuli inspection and utilizes electrical resistivity tomography (ERT) as a noninvasive and powerful tool for inspecting these enigmatic structures, using a nonconventional array. Tumuli, spanning various shapes and sizes, serve as repositories of cultural and funerary traditions, and understanding their internal composition is crucial for unravelling historical narratives. ERT has emerged as a promising geophysical method for investigating subsurface structures, including tumuli. By imaging the electrical resistivity of the ground, ERT enables archaeologists to map variations in soil composition and identify buried features without excavation. This paper reviews the principles of ERT and its application in tumulus studies, showcasing a case study where ERT has successfully revealed internal structures, burial chambers and associated artefacts. The use of 2D ERT is common in tumuli inspection, ignoring accurate 3D effects from the topography. Here we highlight the benefits of the 3D inversion, while we provide a different way to measure which is cost efficient and provides increased spatial resolution to the area of interest. The integration of 3D ERT into archaeological investigations not only enhances our understanding of tumuli construction but also preserves these cultural heritage sites by minimizing the need for invasive excavation. This research contributes to the evolving methodologies in archaeology, emphasizing the synergy between modern technology and traditional archaeological inquiry to uncover the secrets held within tumuli.

Electrical resistivity tomography (ERT) is nowadays widely used in archaeological prospection. This study deals with an approach to make ERT more time-efficient and flexible. It is based on calculating arbitrary 4-point configurations by superposition of multiple pole–pole measurements. Investigating its applicability for archaeological purposes is the objective of this work. To do so, a synthetic study and a case study are conducted to gain insights into effects of nonideal field conditions, noise susceptibility and other challenges during processing and interpretation. Remains of an early modern manor in Noer served as an exemplary object of investigation. Their high resistivity contrast in relation to the surrounding soil makes them ideal for a functionality test. Beforehand, ground penetrating radar measurements were carried out to constrain the forward model used in the synthetic study. It turns out that the pole–pole conversion is well applicable for archaeological prospection under some conditions. The synthetic study shows that the approach is relatively prone to systematic errors. Therefore, it is recommended to locate the external electrodes at a distance of at least 0.7 times (preferably 1.7 times) the maximum internal electrode spacing from the area of investigation. Other error sources like nonideal electrode coupling must be excluded to keep relative noise levels below 1%. The pole–pole conversion can be considered reliable for absolute noise levels below 0.3 mV. Therefore, an A/D converter resolution of, for example, 16-bit should be sufficient for a dynamic range of ±10 V. If all conditions are met, the pole–pole conversion has a great potential to make ERT more time-efficient (up to 50%, depending on configuration sets) and flexible, as it allows to calculate nearly every arbitrary 4-point configuration in the given setup. Combined with optimization approaches like the ‘Compare R’ method, data sets can also be adapted for specific (archaeological) questions or conditions.

This paper presents the interdisciplinary investigation (archaeology, geochemistry, history) of a medieval silver and lead production site located in southern France, in the Minier valley (Occitanie, Aveyron, Le-Viala-du-Tarn). In order to identify the production sites, in situ geochemical surveys were carried out using a portable X-ray fluorescence spectrometer and differential GPS, guided on the analysis of medieval archival sources. The cartographic representation of the metal concentrations in the surface horizons shows significant enrichment of zinc and lead in the vicinity of the mines. This first type of enrichment makes it possible to highlight the activities of separation of sphalerite and silver-bearing galena. The galena thus isolated on the hillsides is then transported to the vicinity of watercourses, where it is crushed, washed, and smelted. These secondary activities result in a last type of enrichment in which only lead is found in large quantities. The cross-referencing of the information made it possible to overcome the challenges related to the location of the mineral processing workshops, which were often invisible on the surface. The medieval workshops have been located and a function suggested, outlining the first trends in the spatial and social division of labour and providing a solid corpus for future archaeological excavations. Finally, this study highlights the persistence of significant metal contamination in the soils of a rural valley and encourages the consideration of former mining areas when examining the environmental impact of metal production.

There is a common agreement among archaeologists that assessing visibility in the field is essential to measure the accuracy of their observations. Archaeologists widely expect that low visibility negatively impacts the recovery rate of artefacts and sites during field-walking surveys. However, they hold fundamentally divergent opinions on using recorded visibility values and on whether or how to weight the results. In this paper, I undertake a review and comparison of ground visibility assessments from three archaeological field-walking surveys conducted in the eastern Mediterranean, all of which have published their data. Capitalizing on the availability of open data, I recode and analyse the algorithms employed in these surveys. The results highlight the impacts of weighting techniques, and I compare the maps produced with and without weighting. In all cases, the corrections do not substantially change the interpretations of the results at the scale of site identification. As such, this data-driven experiment contributes to the ongoing debate on how to compare effectively and integrate data from various survey projects to study regional trends.

The construction history and subsequent usage of burial mounds are an important testimony for socio-economic transformation in prehistoric societies. The Baalberge–Schneiderberg burial mound, subject of the presented study, falls in this category as it is considered as an important monument that indicates the emergence of early social stratification during the Chalcolithic period in central Europe. This hypothesis relies on the chronological development of the burial mound, which is not fully understood until now. Therefore, a reconstruction of the complex stratigraphy of the burial mound including construction phases and later alterations is highly relevant for archaeological research, but the required excavations would be onerous and inconsistent with preservation efforts. In this paper, we demonstrate that non-invasive geophysical prospection, especially seismic sounding with shear and Love waves, is suitable to obtain the required stratigraphic information, if seismic full waveform inversion (FWI) and reflection imaging are applied. Complementary information on the preservation state of the mound is obtained through Electrical Resistivity Tomography (ERT) and Electromagnetic Induction (EMI) measurements. To support the seismic and geoelectric results, we utilize Dynamic Testing (DynP), geoarchaeological corings, ¹⁴C-Dating and archaeological records. Our investigations reveal two construction phases of the Baalberge–Schneiderberg mound. The ¹⁴C-Dating yields dates for the older burial mound that are contemporary to the Chalcolithic Baalberge group (4000–3400 bc). During the Early Bronze Age (EBA), the mound was enlarged to its final size by people of the Aunjetitz/Únětice society (2300–1600 bc). However, both seismic and geoelectric depth sections show an extensive disturbance of the original stratigraphy due to former excavations. For this reason, the exact shape of the older burial mound cannot be determined exactly. Based on our data, we estimate that its height was below 2 m. In consequence, the original Baalberge burial mound was less monumental as until now assumed, which potentially prompting a revision of its significance as indicator for social differentiation.

The Great Tumulus of Apollonia in northern Greece, with a diameter of ~100 m and a height of 19 m, is among the largest of its kind in the region of the ancient kingdom of Macedonia. It is located north of the ancient city of Apollonia and recently became the focus of limited archaeological excavations, which revealed a looted Macedonian tomb. Archaeological findings and other evidence from the tumulus and its surroundings suggest that it may have been used more than once; therefore, the existence of more tombs in its interior is possible. In this work, we investigate the internal structure of the monument by means of 3-D seismic travel time tomography. Using direct sparse methods, we calculate efficiently the full model resolution matrix that allows us to investigate the robustness of the tomographic model. Our results suggest a complex structure with variable properties between the east and the western side of the tumulus. We also detect several regions that may be associated with additional burial locations or other possible targets of archaeological interest.

Recent advancements in remote sensing and artificial intelligence can potentially revolutionize the automated detection of archaeological sites. However, the challenging task of interpreting remote sensing imagery combined with the intricate shapes of archaeological sites can hinder the performance of computer vision systems. This work presents a computer vision system trained for efficient hillfort detection in remote sensing imagery. Equipped with an adapted multimodal semantic segmentation model, the system integrates LiDAR-derived LRM images and aerial orthoimages for feature fusion, generating a binary mask pinpointing detected hillforts. Post-processing includes margin and area filters to remove edge inferences and smaller anomalies. The resulting inferences are subjected to hard positive and negative mining, where expert archaeologists classify them to populate the training data with new samples for retraining the segmentation model. As the computer vision system is far more likely to encounter background images during its search, the training data are intentionally biased towards negative examples. This approach aims to reduce the number of false positives, typically seen when applying machine learning solutions to remote sensing imagery. Northwest Iberia experiments witnessed a drastic reduction in false positives, from 5678 to 40 after a single hard positive and negative mining iteration, yielding a 99.3% reduction, with a resulting F₁ score of 66%. In England experiments, the system achieved a 59% F₁ score when fine-tuned and deployed countrywide. Its scalability to diverse archaeological sites is demonstrated by successfully detecting hillforts and other types of enclosures despite their typical complex and varied shapes. Future work will explore archaeological predictive modelling to identify regions with higher archaeological potential to focus the search, addressing processing time challenges.