## 3 Methodological Approach, Data Acquisition and Processing
For GPR investigation in both cases, all radar reflections were recorded digitally in the field as 16-bit data, 512 (for IDS) and 628 (for MALA) samples per radar scan at 25 scan s\({}^{-1}\) for IDS (1 scan approximately corresponds to 0.025 m) and 24 scan s-1 for MALA (1 scan approximately corresponds to 0.024 m). Half meter equally spaced GPR profiles were acquired in grids adapted to the available areas. EMI measurements were collected in stationary(point-to-point) mode using frequencies in the range 5-16 kHz with vertically oriented dipoles. The investigation area was covered by profiles spaced 0.5 m inserted in a regular grid. * Choice of the techniques of data processing for the selected methodologies:
* For ERT, the measured apparent resistivity datasets were processed in order to remove dragging effects that are typical of DD array and model the survey targets converting the values in real electrical resistivity values displayed as a function of depth below surface. To this end, a probability tomography approach has been applied for imaging the sources of anomalies into the analyzed grounds. The theory was first stated for the self-potential method [58], and then adapted to the resistivity method [59; 60; 61]. The primary approach, even if capable of distinguishing resistivity highs and lows in the field datasets considering a reference background resistivity, precluded the estimate of the intrinsic resistivities of the source bodies. The method was successfully used to delineate the geometry of the self-potential sources in the central volcanic area of Vesuvius (Naples, Italy) [62], to imaging through the resistivity method buried archaeological structures in the archeological site of Pompei (Naples, Italy) [63], at the Castle of Zena (Carpeneto Piacentino, Italy) [64], the prehistoric sites of Checua (Cundinamarca, Colombia) [65] and Grotta Reali (Rocchetta a Volturno, Italy) [66], the Archaeological Park of Aeelanum [67] and for fault detection in a site on the Matese Mountain (Italy) [3]. Successively, a data-adaptive probability-based ERT inversion (PERTI) method [51] was directly derived from the principles of the probability tomography in order to estimate the true resistivities. From a probabilistic point of view, the algorithm, being a non-linear approach, identifies, inside the set of possible solutions the most probable one, compatibly with the dataset acquisition scheme. The main features of the PERTI method, as reported in [51] are: (i) unnecessity of a priori information; (ii) full, unconstrained adaptability to any kind of dataset, including the case of non-flat topography; (iii) drastic reduction of computing time of even two orders of magnitude, with respect to the previous methods in complex 3D cases using the same computer; (iv) real-time inversion directly in the field, thus allowing for fast modifications of the survey plan to better focus the expected targets; (v) full independence from data acquisition techniques and spatial regularity, (vi) possibility to be used as an optimum starting model in standard iterative inversion processes in order to speed up convergence. As the PERTI method does not require a priori information and iterative processes, the computation of the route mean square (RMS) error between measured and modelled apparent resistivity values is useless. In fact, the resulting RMS error, whatever it is, can be lowered within the PERTI scheme. Many applications of the PERTI approach in near surface prospections are available in literature for solving archaeological research questions [49; 68; 69; 70; 71], for defining faults in Crete [72] and for imaging of the near-surface structure of the Solfatara crater, Campi Flegrei (Naples, Italy) [73]. In [49; 51; 68; 72] the PERTI routine was tested using well-known commercial software of inversion of geoelectrical data and the comparison put in evidence for the coherence between the obtained results and the better filter capacity and great versatility of the PERTI algorithm.