Over the past decade, high-resolution noninvasive sensors have been widely used in explorations of the first few meters underground at archaeological sites. However, remote sensing actions aimed at the study of structural elements that require a very high resolution are rare. In this study, layer characterization of the floor mosaic substrate of the Pisões Roman archaeological site was carried out. This work was performed with two noninvasive techniques: 3D ground penetrating radar (3D GPR) operating with a 1.6 GHz central frequency antenna, which is a very high-resolution geophysical method, and photogrammetry with imagery obtained by an unmanned aerial vehicle (UAV), which is a very high-resolution optical method. The first method allows penetration up to 30-40 cm depth and 3D models can be obtained, and with the second method, very high detail surface images and digital surface models can be obtained. In this study, we analyze a combination of data from both sensors to study a portion of the floor mosaic of the Pisões Roman Villa (Beja, Portugal) to obtain evidence of the inner structure. In this context, we have detected the main structural levels of the Roman mosaic and some internal characteristics, such as etched guides, internal cracking, and detection of higher humidity areas. The methodology that we introduce in this work can be referenced for the documentation of ancient pavements and may be used prior to carrying out preservation activities. Additionally, we intend to show that a Roman mosaic, understood as an archaeological structure, does not consist of only beautiful superficial drawings defined by the tesserae, but these mosaics are much more complex elements that must be considered in their entirety for preservation.
Usually, in ground-penetrating radar (GPR) datasets, the user defines the limits between the useful signal and the noise through standard filtering to isolate the effective signal as much as possible. However, there are true reflections that mask the coherent reflectors that can be considered noise. In archaeological sites these clutter reflections are caused by scattering with origin in subsurface elements (e.g., isolated masonry, ceramic objects, and archaeological collapses). Its elimination is difficult because the wavelet parameters similar to coherent reflections and there is a risk of creating artefacts. In this study, a procedure to filter the clutter reflection noise (CRN) from GPR datasets is presented. The CRN filter is a singular value decomposition-based method (SVD), applied in the 2D spectral domain. This CRN filtering was tested in a dataset obtained from a controlled laboratory environment, to establish a mathematical control of this algorithm. Additionally, it has been applied in a 3D-GPR dataset acquired in the Roman villa of Horta da Torre (Fronteira, Portugal), which is an uncontrolled environment. The results show an increase in the quality of archaeological GPR planimetry that was verified via archaeological excavation.
Different geophysical methods applied at the settlement of Villasviejas del Tamuja (Botija, Spain) have identified robust anomalies located at the same position, but some anomalies are reflected by only one method. Furthermore, analysing the spatial correlation of these anomalies is of fundamental importance for obtaining a correct archaeological interpretation. In this work, we analysed the main results of electrical resistivity tomography (ERT), ground-penetrating radar (GPR) and magnetic gradiometry methods in a particular area of the archaeological site. In this analysis, we performed graphical and numerical spatial correlation analyses of the anomalies and observed strong agreement among the results provided by each method. Certain anomalies were reflected only in the magnetic and ERT studies. The results highlight the importance of applying several geophysical methods and performing spatial correlational analyses. Furthermore, the methodology that we have applied to evaluate the spatial correlation offers interesting results.
The use of muons for geophysical surveys has been proved successful in numerous projects around the planet. The use of muography in an underground environment has an easy side, when compared to the surface, due to the absence of the background radiation. On the other hand, the muon flux is much lower than what is measured on the surface. Geological and underground conditions should be considered when defining the required exposure time and developing suitable muon telescopes for the observation. A collaboration has been established between the Institute of Earth Sciences (ICT), University of Évora, the Laboratory of Instrumentation and Experimental Particle Physics (LIP), and the Lousal Ciência Viva Center to develop muon detectors and evaluate the muography potential in the Lousal Mine, with the general aim to create the conditions to use muography as a novel method for geophysical surveys in Portugal. The Lousal Mine (Iberian Pyrite Belt) was exploited until 1988 and is presently an excellent European example of environmental rehabilitation and social improvement based on museum, scientific, and educational activities. The observations are done from the Waldemar mine gallery, about 18 m below the surface. The telescopes, developed by LIP, use robust RPC detectors to observe the crossing muons in real time. The aim is to do a first geological survey of the region with muography, mapping already known structures and ore lenses and measuring their densities. The new data will then be used to improve the existing information, but the full process also serves to test the performance of the muon telescope and of the muography analysis tools. A reference 3D model is being created by joining pre-existing geological and geophysical information and new measurements, done, namely, with seismic refraction and Ground Penetrating Radar (GPR). This model provides a reference against which to compare the muography results. Ideally, muography could be used to produce an equivalent 3D map of densities. This reference 3D model constructed with independent methods will be used to cross-check the muography results.
This work addresses the problem of the lack of perceptibility that geophysical data may have. Data fusion allows us to combine datasets, providing an improved and more informative source of information about structures buried in the ground. After testing different approaches, a strategy was developed using ground-penetrating radar and magnetic datasets collected over the same area. Data collected at the Roman Villa of Pisões (Beja, Portugal), which is a place of easy application of geophysical methods, were used to test the method, but with problems caused by the properties of the soil. The approach was based on processing operations that allow the fusion of images obtained by different equipment widely used in medical imaging for tumor detection and image processing. The goal is to create an improved image with data fusion that has higher quality than the input images, allowing a better understanding of the object of the study. The approach is composed of two stages: pre-processing and data fusion. Pre-processing is applied to enhance the input data. It consists of removing background noise through singular value decomposition applied in the spectral domain. Then the calculation of the data entropy will highlight the differences corresponding to the spatial alignments compatible with buried structures. Then, both entropy maps of the two datasets are fused in the second processing step to produce the final image. This step involves applying the 2D wavelet transform to each entropy map, decomposing them into sub-bands. Algorithms to calculate multiresolution singular value decomposition and the image gradient are applied to the sub-bands. The processed sub-band pairs are then fused using specific fusion rules. The fused image is obtained by applying the inverse of the wavelet transform. Data fusion with the proposed approach allows us to obtain a detailed image that is sharper and of better quality than the input datasets. The increase in sharpness and quality can be quantified through the sharpness index and the BRISQUE quality index in several steps of the processing. The obtained values confirm the graphical results. Images produced by the proposed data fusion approach suggest that the perceptibility has increased, allowing us to provide conclusions about the existence of buried structures.
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