# Optimised Extraction of Archaeological Features from Full 3-D GPR Data
## 4 Results  
After the standard processing flow, some structures that were well correlated were imaged, but their actual shape and extension were difficult to define (Figure 2). We therefore focused on the optimization of the migration process, which is an essential step to improving the lateral resolution and approximating the true shape and geometry of the buried elements. In Figure 3 we show how different results were obtained by using different constant velocity values from 7 to 12 cm/ns for migration. It is quite apparent that velocities of 9 and 10 cm/ns produced very similar results, while both lower and higher velocities (Figure 3A,D, respectively) produced very degraded results. In particular, a velocity of 7 cm/ns produced a sufficient imaging of the circular structure, while the semi-circular one cannot be detected; the opposite occurs for a velocity of 12 cm/ns, demonstrating how it is difficult to obtain a reliable interpretation when the velocity field is not correct and when spatial sampling is not adequate [44]. Integrated analysis of 2-D inlines and crosslines of the migrated volumes and of time-slices is the typical strategy adopted to interpret the features and amplitude anomalies of potential archaeological interest in GPR data. Exemplary results obtained for localized structures (bases of columns) and for planar elements (a stone floor) are reported in Figures 4 and 5, respectively. The importance of 3-D slices, which can be eventually calculated considering the mean values within vertical windows of a few samples, is easily detectable by analysing the two examples. In particular, localized structures are elusive on 2-D profiles (B-scans) even when the profiles perfectly cross several constant spaced elements. This demonstrated once more that the performance of full 3-D datasets, compared to those of 2-D and 2.5-D surveys, is better. Moreover, even when the archaeological structures are regularly spaced and identical from the building point of view (Figure 4), on single GPR profiles they are different due to various levels of conservation and/or to collapsed materials, while they are very clear on time- or depth-slices. Wide planar elements can be detected on 2-D profiles more easily due to their extension and if they are characterized by high amplitude (or more generally by a strong amplitude contrast). On volume-slices, the amplitude spans over positive and negative values (Figure 5B), sometimes making interpretation difficult. This behaviour is not surprising because the irregular time/depth of the reflectors produces various phases when it is sliced. This is further emphasized when non-homogeneous velocity fields are present, as in almost all the cases in our test site. In fact, time-slices by definition cannot take into account this behaviour, while depth-slices can be distorted when the actual velocity field differs from the one used for migration/depth conversion. We tested different attributes including texture, coherency, and amplitude-based attributes, not always obtaining remarkable results. In Figure 6, three texture attributes [39] were calculated on the same depth-slice. It is remarkable to notice that attributes have a strong potential, but are very sensitive to the SNR, which is often low in GPR datasets of archaeological sites [45]. Some attributes (such as the Chaos one) are able to highlight not only the edge of buried structures, but also their extension. In addition, when single apparent structures are not present, attributes can help in the qualitative identification of \"homogeneous\" zones, which in turn can have archaeological potential. It is not sufficient to choose which attributes are the more suitable for data analysis, which is both subjective and strictly site-dependent; it is more important to select the appropriate parameters to use during the calculation. Coherence attributes are one of the most powerful attribute categories when applied on full 3-D data [42], this has been well known for many years in the study of reflection seismic data [46].