GNGTS 2017 - 36° Convegno Nazionale

GNGTS 2017 S essione 2.2 405 H/V ratios amplitude of the station UMSG is around 0.5. This probably might be due to the major contribution of the Rayleigh waves in the shallower layers. We clearly observe that, for more than one station, the H/V ratios are lower than one mainly after 4 Hz. For the evaluation of the H/V M ratios (Fig. 2d), we divided the stations in two groups; the first group shown in Fig 2d is characterized by a high variability, principally at the higher frequencies of the entire range of analysis, and the second group shown in Fig. 2e is instead typified by a high variability mostly concentrated in the frequency range of 1-8 Hz. The stations of the second group, CELG, PESG, RENG, SESG, SETG and VIRG, are located in the area identified by Chiodini et al. (2016) and by De Siena et al. (2017) as the zone with fumaroles, that are directly linked to the 4 km deep melted rock through the vaporization of the liquid of meteoric origin forming a 2-km-deep vertical plume of gas. Array analysis and inversion. To obtain a seismic surface velocity model we applied the MSPAC method (Bettig et al., 2001) to the noise recordings of the array ARF, given that this method allows to compute the spatial autocorrelation even if the array geometry is not semi- circular. We divided the array in semi-circular sub arrays called Rings, which radius are defined by the sensor’s spacing, and we calculated the spatial autocorrelation for all the possible pairs of sensors. Each Ring is the result of an appropriate balance between the number of sensor pairs per Ring (as large as possible) and the thickness of the Ring (as small as possible). Thus our geometry is composed by 13 Rings, ranging from a minimum of 54 m to a maximum of 410 m spacing of the sensors, for a total of 36 sensor pairs. For each Ring the autocorrelation coefficients are averaged over 350 time windows and over all the sensors pairs (Fig. 3). We used the DINVER computer code (Wathelet et al. , 2004) to perform a joint inversion of the autocorrelation coefficients (Fig. 3a) and the mean H/V ratio (Fig 3b), calculated on Coda Waves of the local and regional events recorded at the station CELG. The result of this inversion, shown in the Fig. 3c, provides a shallow shear wave velocity model with a misfit of 1.1. This model, valid for the first 200 m, is a complementary information to the velocity models obtained from the available tomographies that have a resolution of 500 m, and together they will be the starting velocity model for the inversion of the H/V M . Conclusions. The H/V analyses performed with scattered wavefield data do not show a clear dependency on the earthquake azimuth, even if for frequency lower than 1 Hz most of the stations show differences in the frequency and in the amplitude of the peaks. This behaviour mainly affects the H/V ratios of the local earthquakes. The H/V M analysis highlights that the stations surrounding the Solfatara crater exhibit high standard deviations under 8 Hz, that might be due to the presence of the melted rock zone and the vertical gas plume, that influences the azimuthal dependence of the ratios. This assumption could be verified using a joint inversion procedure, with a starting model derived from the integration of the tomographic large scale velocity models with other information coming from gravimetric and magnetic studies, deep drilling and noise based monitoring. The small scale velocity model, obtained applying the array technique MSPAC method to the seismic noise, is a good starting point for the future inversions. References Afanasyev A., Costa A., Chiodini G.; 2015: Investigation of hydrothermal activity at Campi Flegrei caldera using 3D numerical simulations: Extension to high temperature processes. J. Volcanol. Geotherm. Res., 299 , 68-77. Aster R., Meyer R., De Natale G., Zollo A., Martini M., Del Pezzo, E., Scarpa R., Iannaccone G.; 1992: Seismic in- vestigation of the Campi Flegrei: a summary and synthesis of results. Volcanic Seismology, 462-483. Battaglia J., Zollo A., Virieux J., Dello Iacono D.; 2008: Merging active and passive data sets in traveltime tomogra- phy: The case study of Campi Flegrei caldera (Southern Italy). Geophysical Prospecting, 56 , 555-573. Bettig B., Bard P.Y., Scherbaum F., Riepel F., Cotton F., Cornou C., Hatzfeld D.; 2001: Analysis of dense array noise measurements using the modified spatial auto-correlation method (SPAC): application to the Grenoble area. Boll. Geof. Teor. Applic. 42, 281-304. Capuano P., Russo G., Civetta L., Orsi G., D’Antonio M., Moretti R.; 2013: The active portion of the Campi Flegrei caldera structure imaged by 3-D inversion of gravity data. Geochem. Geophys. Geosyst., 14 , 4681–4697.