No abstract

This paper describes in detail the development of a ground-penetrating radar (GPR) model for the acquisition, processing and visualisation of underground utility infrastructure (UUI) in a controlled environment. The initiative was to simulate a subsurface urban environment through the construction of regional road, local road and pedestrian pavement in real urban field/testing pools (RUTPs). The RUTPs represented a controlled environment in which the most commonly used utilities were installed. The accuracy of the proposed kinematic GPR-TPS (terrestrial positioning system) model was analysed using all the available data about the materials, whilst taking into account the thickness of the pavement as well as the materials, dimensions and 3D position of the UUI as given reference values. To determine the reference 3D position of the UUI, a terrestrial geodetic surveying method based on the established positional and height geodetic network was used. In the first phase of the model, the geodetic network was used as a starting point for determining the 3D position of the GPR antenna with the efficient kinematic GPR surveying setup using a GPR and self-tracking (robotic) TPS. In the second phase, GPR-TPS system latency was quantified by matching radargram pairs with a set of fidelity measures based on a correlation coefficient and mean squared error. This was followed by the most important phase, where, by combining sets of “standard” processing routines of GPR signals with the support of advanced algorithms for signal processing, UUI were interpreted and visualised semi-automatically. As demonstrated by the results, the proposed GPR model with a kinematic GPR-TPS surveying setup for data acquisition is capable of achieving an accuracy of less than ten centimetres.

In 2005, during the annual ‘intensive’ archaeological survey in the peri-urban zone of Sagalassus (Pisidia, southwest Anatolia), the remains of an extensive Classical-Hellenistic settlement were identified at Düzen Tepe, 1.8km southwest of Sagalassus. The results of three seasons of site investigation (2005–2007), comprising archaeological and geophysical surveys, architectural and topographical mapping, test soundings, and archaeometric and environmental research, are presented here, together with a discussion of the settlement's most probable socio-economic background. According to the latest results of all excavated or sampled survey ceramic artefacts and AMS C14 dating of faunal remains, the (proto-)urban settlement at Düzen Tepe was inhabited during the fifth to second century BC. It is the first of its kind to shed light on the material culture of the ancient inhabitants of the region, the ‘Pisidians’.

Kamnolom v naselju Podpeč pri Ljubljani velja za glavni kamnolom rimske kolonije Emone (Ljubljana). Obstoj antičnega kamnoloma v Podpeči je bil do sedaj utemeljen le na podlagi makroskopske podobnosti med Členom litiotidnega apnenca, ki izdanja v Podpeči, in z njim povezanimi rimskimi spomeniki, odkritimi v Ljubljani. V okviru pričujočega dela smo skušali poiskati oprijemljive arheološke in geoarheološke dokaze za njegov obstoj. V skrajno severnem delu kamnoloma smo izvedli arheološko sondiranje, hkrati pa natančno določili sestavo apnenca na tem območju in v 288 izdelkih iz antične Emone. Sondiranje severno od modernega kamnoloma je odkrilo dobro ohranjene sledove rimskodobne kamnoseške dejavnosti. Hkrati je litološka analiza spomenikov iz Emone razkrila, da je kar 182 (ali 64 odstotkov) analiziranih spomenikov izdelanih iz apnenca, litološko identičnega različkom, ki izdanjajo v Podpeči. Ti različki se prostorsko grupirajo severno od modernega kamnoloma. Pri tem sicer ostaja odprto vprašanje pripadnosti nekaterih različkov apnenca v izdelkih, ki so brez diagnostičnih komponent in bi lahko pripadali tudi nekoliko starejšemu Členu krkinega apnenca. Rekonstrukcijo obsega starega kamnoloma omogoča primerjava faciesov izvornih litostratigrafskih enot in izdelkov, pa tudi historična analiza zgodnjih kartografskih in katastrskih dokumentov. Analiza najzgodnejših izdelkov je omogočila hipotezo o prihodu kamnosekov iz kamnoloma v Nabrežini.

Several studies in the field of archaeological prospection have suggested that multi-element soil geochemical survey holds potential as an archaeological survey method, complementing more common techniques such as magnetometry and fieldwalking. Despite this, only limited research regarding this topic has been carried out. In this study, a geochemical survey was conducted in the Eastern Suburbium of the Roman to Byzantine city of Sagalassos, with the aim of investigating the possibilities and limitations of the technique in facilitating the interpretation of magnetic survey data. To explore the multivariate patterns present within the chemical dataset, the data were statistically analysed using a contiguity-constrained spatial clustering algorithm. The resulting spatial clusters revealed that an area characterized by high-frequency magnetic anomalies was spatially associated with a chemical cluster rich in Cu, K, P, Pb and Zn. This is interpreted as resulting from the accumulation and decomposition of occupational debris. Enhancements of Al, As and Ba helped to interpret an area with a low magnetic signal as a region where limestone bedrock was located close to the soil surface. Finally, two zones of enhanced magnetization were shown to spatially overlap with two clusters of soils containing elevated levels of Co, Cr, Mg, Mn, Ni and (Fe) and Ti, V and (Fe), respectively, indicating the presence of two different types of mafic to ultramafic ophiolitic bedrock near the soil surface. This study confirms that multi-element geochemistry has potential as a survey technique, because it offers direct information on soil bedrock or ancient human disturbance, thereby adding an extra dimension to the interpretation of geophysical survey data. It is shown that soil chemical data are particularly valuable for distinguishing anthropogenic and natural impacts creating geophysical anomalies. Contiguityconstrained clustering was found to be a promising data analysis technique for multivariate geochemical survey data.