Wenke Zhao

Application of 3D GPR attribute technology in archaeological investigations

Ground penetrating radar (GPR) attribute technology has been applied to many aspects in recent years but there are very few examples in the field of archaeology. Especially how can we extract effective attributes from the two- or three-dimensional radar data so that we can map and describe numerous archaeological targets in a large cultural site? In this paper, we applied GPR attribute technology to investigate the ancient Nanzhao castle-site in Tengchong, Yunnan Province. In order to get better archaeological target (the ancient wall, the ancient kiln site, and the ancient tomb) analysis and description, we collated the GPR data by collected standardization and then put them to the seismic data processing and interpretation workstation. The data was processed, including a variety of GPR attribute extraction, analysis, and optimization and combined with the archaeological drilling data. We choose the RMS Amplitude, Average Peak Amplitude, Instantaneous Phase, and Maximum Peak Time to interpret three archaeological targets. By comparative analysis, we have clarified that we should use different attributes to interpret different archaeological targets and the results of attribute analysis after horizon tracking is much better than the results based on a time slice.

Early Roman military fortifications and the origin of Trieste, Italy

Significance Archaeological evidence from the Trieste area (Italy), revealed by airborne remote sensing and geophysical surveys, provides one of the earliest examples of Roman military fortifications. They are the only ones identified in Italy so far. Their origin is most likely related to the first year of the second Roman war against the Histri in 178 B.C., reported by Livy, but the sites were in use, perhaps not continuously, at least until the mid first century B.C. The main identified San Rocco military camp is the best candidate for the site of the first Trieste. An interdisciplinary study of the archaeological landscape of the Trieste area (northeastern Italy), mainly based on airborne light detection and ranging (LiDAR), ground penetrating radar (GPR), and archaeological surveys, has led to the discovery of an early Roman fortification system, composed of a big central camp (San Rocco) flanked by two minor forts. The most ancient archaeological findings, including a Greco–Italic amphora rim produced in Latium or Campania, provide a relative chronology for the first installation of the structures between the end of the third century B.C. and the first decades of the second century B.C. whereas other materials, such as Lamboglia 2 amphorae and a military footwear hobnail (type D of Alesia), indicate that they maintained a strategic role at least up to the mid first century B.C. According to archaeological data and literary sources, the sites were probably established in connection with the Roman conquest of the Istria peninsula in 178–177 B.C. They were in use, perhaps not continuously, at least until the foundation of Tergeste, the ancestor of Trieste, in the mid first century B.C. The San Rocco site, with its exceptional size and imposing fortifications, is the main known Roman evidence of the Trieste area during this phase and could correspond to the location of the first settlement of Tergeste preceding the colony foundation. This hypothesis would also be supported by literary sources that describe it as a phrourion (Strabo, V, 1, 9, C 215), a term used by ancient writers to designate the fortifications of the Roman army.

Texture Attribute Analysis of GPR Data for Archaeological Prospection

We evaluate the applicability and the effectiveness of texture attribute analysis of 2-D and 3-D GPR datasets obtained in different archaeological environments. Textural attributes are successfully used in seismic stratigraphic studies for hydrocarbon exploration to improve the interpretation of complex subsurface structures. We use a gray-level co-occurrence matrix (GLCM) algorithm to compute second-order statistical measures of textural characteristics, such as contrast, energy, entropy, and homogeneity. Textural attributes provide specific information about the data, and can highlight characteristics as uniformity or complexity, which complement the interpretation of amplitude data and integrate the features extracted from conventional attributes. The results from three archaeological case studies demonstrate that the proposed texture analysis can enhance understanding of GPR data by providing clearer images of distribution, volume, and shape of potential archaeological targets and related stratigraphic units, particularly in combination with the conventional GPR attributes. Such strategy improves the interpretability of GPR data, and can be very helpful for archaeological excavation planning and, more generally, for buried cultural heritage assessment.

Integrated attribute analysis for improved GPR data interpretation

We apply integrated attribute analysis to extract more reliable quantitative information from processed GPR data, and obtain a better characterization of subsurface structures. A multi-attribute approach is used to characterize the subsurface through different attribute categories, including instantaneous, coherency, and textural attributes applied to quantities related not only to amplitude, phase, and frequency, but also to other parameters calculated from the original data. The different attributes can be integrated into a single view with composite displays (overlays and mixed displays). The proposed procedure is tested in different environments namely: archaeological areas to characterize cultural heritage buried in highly heterogeneous subsurface environments, and glaciers to image their inner structure and to monitor the seasonal changes of firn layers. The results from two case studies demonstrate that the proposed integrated attribute analysis can highlight zones characterized by different electromagnetic parameters, better visualize and quantify GPR features in an automatic and objective manner, and enhance GPR data interpretation in different application fields.