GNGTS 2022 - Atti del 40° Convegno Nazionale

432 GNGTS 2022 Sessione 3.2 employing a sampling frequency of 100 Hz. Time alignment of samples and positioning were guaranteed by a GPS. We processed the ambient noise signals using the open-source software Geopsy (Wathelet et al. , 2020) to obtain the HVSR curves and the resonance frequency, and to extract the directivity information of the signals by plotting the spectral ratio as a function of both frequency and azimuth. Data processing was performed following the guidelines and recommendations of the SESAME project (Bard et al. , 2004). Results. The HR seismic profile A-B, shown in Fig. 2a, highlights two main reflections, due to the river bed (blue line) and the unconformity surface between Pleistocene/Holocene clayey sediments and PGS gravels (green line), together with several blank zones, likely associated with gas accumulation. Conversely, there are no pieces of evidence of major faults in the shallow subsurface along the investigated line, as well as on the river bed (Fig.1b). The inverted model of the L2 line (Fig. 2b) gives information about the electrical properties of groundwater (salinity) and natural gases in the study area. We detect a moderately resistive layer (20-30 Ωm) down to 15 m b.s.l., while resistivity abruptly decreases to 0.5-1.5 Ωm between 15 and 55 m b.s.l., due to the presence of saline intrusion inland. Then, we observed a resistivity increase in the gravel PGS formation, which is known to host the gas reservoir. A slight increase in the resistivity of the middle layer (ρ >1.5 Ωm) can be likely attributed to the Fig. 2 - (a) HR seismic sub-bottom profile A-B. (b) Resistivity model for the L2 line. (c) Resistivity model for the L4 line, where HR seismic sub-bottom profile A’-B’ and current density model are superposed. Gas upwelling flows are marked with black arrows.