GNGTS 2019 - Atti del 38° Convegno Nazionale

244 GNGTS 2019 S essione 1.4 abundances ( α U = α Th = 1 µg/g). The expected geoneutrino signal linearly scales with the U and Th mass distributed in the crust and depends on the source-detector distance r by a combined effect of the 1/4 π r 2 spherical scaling factor and the average antineutrino survival probability, which oscillations gradually damp for increasing distance from the experimental site. Based on the approach and input parameters described in Sect. 7 of Strati et al. (2017), we calculated the geoneutrino signal expressed in Terrestrial Neutrino Units (TNU) (Fiorentini et al. , 2007). From the comparison between the signals calculated using the prior and GIGJ models, it is possible to infer that the benefit of using gravity information with the proposed inversion procedure, led to a site-specific repartition of the signal contribution from deep layers (MC and LC) together with their uncertainty. Conclusions. GIGJ is a 3D numerical model constituted by ~46×10 3 voxels of 50 × 50 × 0.1 km. GIGJ fitted homogeneously distributed GOCE gravity data with a standard deviation of the residuals of the order of 1 mGal, compatible with the observation accuracy. The solution was the smoothest one in terms of both density distribution and geometrical shape. Regarding geoneutrino signals prediction, the main outcome of this study was the 77%, 55% and 78% reduction of the UC, MC and LC signal uncertainty. Because of the rearrangement of the crustal layers thicknesses, we predicted a reduction (~21%) and an increase (~24%) of the MC and LC signal respectively, in comparison with the results obtained from global models. This study demonstrated that a Bayesian-based gravimetric inversion applied to reliable satellite data, rationally integrated with local geological and seismic information, provided a coherent picture of the crustal structure at the natural spatial scale required for geoneutrino studies. Acknowledgements. We would to thanks M. Reguzzoni, M. Baldoncini, I. Callegari, P. Poli, D. Sampietro and F. Mantovani for the support to the research and the JUNO Italian Collaboration for the useful reviews and comments References Azencott R (1988). Simulated annealing. Séminaire Bourbaki (1987-1988), 30, 223-237. Fiorentini G, Lissia M, and Mantovani F (2007). Geo-neutrinos and earth’s interior. Phys. Rep., 453(5-6), 117-172. Gatti A and Reguzzoni M (2017). GOCE gravity field model by means of the space-wise approach (release R5). GFZ Data Services. Huang Y, Chubakov V, Mantovani F, Rudnick RL, and McDonough WF (2013). A reference Earth model for the heat- producing elements and associated geoneutrino flux. Geochem., Geophys., Geosyst., 14(6), 2023-2029. John BM, Zhou XH, and Li JL (1990). Formation and tectonic evolution of Southeastern China and Taiwan: Isotopic and geochemical constraints. Tectonophysics, 183(1-4), 145-160. Mosegaard K and Tarantola A (2002). Probabilistic approach to inverse problems. Int. Geophys., 81, 237-265. Robert CP and Casella G (2004). Monte Carlo Statistical Methods. Rossi L, Reguzzoni M, Sampietro D, and Sansò F (2016). Integrating geological prior information into the inverse gravimetric problem: the Bayesian Approach. In N Sneeuw, P Novák, M Crespi, & F Sansò (Eds), VIII Hotine- Marussi Symposium on Mathematical Geodesy: Proc. of the Symposium in Rome, 17-21 June, 2013 (pp. 317- 324). Cham: Springer Int. Pub. Rott C, Taketa A, and Bose D (2015). Spectrometry of the Earth using Neutrino Oscillations. Sci. Rep., 5, 15225. Strati V, Wipperfurth SA, Baldoncini M, McDonough WF, and Mantovani F (2017). Perceiving the Crust in 3-D: A Model Integrating Geological, Geochemical, and Geophysical Data. Geochem., Geophys., Geosyst., 18(12), 4326-4341. Zheng Y and Zhang S (2007). Formation and evolution of Precambrian continental crust in South China. Chin. Sci. Bull., 52(1), 1-12.