# Multi-Parametric Imaging of Etruscan Chamber Toms:
## 4 Discussion  
The combination of different geophysical techniques reduces the natural ambiguity of each method. In fact, data correlation allows us to reinforce, validate, or sometimes exclude artifacts in the models that were obtained by a single geophysical approach.  
Two different types of integration were proposed by [PERSON] et al. (1999) [PERSON] et al. (1999), one of which involved data fusion or merging, wherein datasets are overlaid to maintain their characteristics. The other involves data integration, wherein data modeling integration returns a new thematic map. In this study, we adopted the first approach because of the different resolutions between the applied techniques and the fact that the measurements were carried out along survey lines due to the rough topography of the site, mainly characterized by diffuse vegetation, which impeded us to approach with a geophysical 3-D grid acquisition.  
The acquisition of the whole geophysical dataset took approximately 2 days and required an additional few days for the data processing. In particular, the CCR profiles required about 2 h and two people for all survey operations.  
Before venturing into the comparison between CCR and ERT output resistivity models, there are some issues to consider in order to avoid misinterpretation of the resistivity models.  
Since CCR sensors are dragged over the ground, the measured data cannot be considered as derived by point sources, as it happens for galvanic electrodes. We should take  
Figure 7: Rn and Th maps. Geochemical maps for the measured (**a**) Th and Rn concentrations: Red and yellow squares show areas of very high and high Rn concentrations, while green and white ones represent low and very low concentrations. Th concentrations are classified into four classes represented by different light-blue circle dimensions; (**b**) Th/Rn ratios: Red and yellow circles indicate areas of very high and high ratios while light-blue and blue circles represent low and very low ratio values.  
into account that the subsoil is not homogeneously sampled along the CCR profile, for instance, due to the presence of obstacles in the surface or simply because the operator does not necessarily walk at the same speed along the line so that the subsoil is not equally sampled as it happens for ERT profiling. Although we average the time-sampled data to a constant nominal electrode spacing, and then we converted the raw voltage measurements to apparent resistivity using a line antenna geometric factor [37], the CCR data will always be incorrectly represented in any electrode-based forward modeling and inversion algorithm [47]. In addition, even in the absence of known noise factors for capacitive methods [48], acquired CCR data will always tend to be noisier than ERT data due to the non-perfect coupling between the sensors and the ground. Such differences in field measurement procedures and noise levels determine that recovered CCR resistivity models will never precisely emulate those recovered from ERT data acquired over coincident survey lines even if the former is calibrated by applying the effective dipole length correction factor [49].  
As for the Rn in soil measurements, it proved to be an easy-to-use and low-cost method in which data acquisition took approximately one day simultaneously using two radiometers by two operators. Note that, although the instrument can be easily carried out on any type of soil (arid, vegetated, flat or steep), measurements cannot be acquired after a rainy period, in wet soils, or outcropping rocks. The survey grid design is strictly a function of the target dimensions, which determines the number of samples and the grid size. Measured data can be used to produce a concentration map able to identify soil anomalies and the results provide a horizontal 2-D relative distribution of the concentration anomalies. Nevertheless, recovered results cannot be used to provide quantitative information about the depth of the gas source.