6533b839fe1ef96bd12a6548
RESEARCH PRODUCT
GIGJ: a crustal gravity model of the Guangdong Province for predicting the geoneutrino signal at the JUNO experiment
Monica SistiAgnese GiazStefano Maria MariA. BrigattiSeverino Angelo Maria BussinoC. SirignanoVito AntonelliF. Dal CorsoD. PedrettiGiuseppe VerdeR. BrugneraRoberto IsocrateG. GaletIvano LippiM. MontuschiDaniele SampietroG. SalamannaMassimiliano NastasiMarco GrassiAntonio BudanoAntonio InsoliaCristina MartelliniAndrey FormozovXuefeng DingAlessandra ReMarco GiammarchiFabio MantovaniG. FiorentiniEnrico BernieriE. MeroniFilippo MariniFatma SawyM. BuscemiD. Lo PrestiAlessandro PaoloniMauro MezzettoMarica BaldonciniFausto OrticaYury MalyshkinDaniele CortiVirginia StratiMirko ReguzzoniSalvatore MonforteGiulio SettantaLuca StancoGiuseppe AndronicoPiero PoliAndrea FabbriGioacchino RanucciDavide ChiesaA. GarfagniniMarco BellatoS. DusiniFabio LonghitanoAldo RomaniR. PompilioM. SpinettiR. FordP. SaggeseLino MiramontiIvan CallegariPaolo LombardiN. PellicciaLorenzo RossiS. ParmeggianoEzio PrevitaliBarbara RicciRossella CarusoLucia Votanosubject
010504 meteorology & atmospheric sciencesGeoneutrinogeophysical uncertaintieInverse transform samplingFOS: Physical sciences01 natural sciencesBayesian methodUpper middle and lower crustStandard deviationNOSouth China BlockmiddlePhysics - GeophysicsMonte Carlo stochastic optimizationGOCE data gravimetric inversionGeophysical uncertaintiesGeochemistry and PetrologyEarth and Planetary Sciences (miscellaneous)Bayesian method; geophysical uncertainties; GOCE data gravimetric inversion; Monte Carlo stochastic optimization; South China Block; upper middle and lower crustImage resolution0105 earth and related environmental sciencesSubdivisionJiangmen Underground Neutrino Observatoryupper and middle and lower crustbusiness.industrySettore FIS/01 - Fisica SperimentaleCrustupperGeodesy[PHYS.PHYS.PHYS-GEN-PH]Physics [physics]/Physics [physics]/General Physics [physics.gen-ph]Geophysics (physics.geo-ph)and lower crustDepth soundingGeophysics13. Climate actionSpace and Planetary SciencebusinessGeologyBayesian method geophysical uncertainties GOCE data gravimetric inversion Monte Carlo stochastic optimization South China Blockupper and middle and lower crustdescription
Gravimetric methods are expected to play a decisive role in geophysical modeling of the regional crustal structure applied to geoneutrino studies. GIGJ (GOCE Inversion for Geoneutrinos at JUNO) is a 3D numerical model constituted by ~46 x 10$^{3}$ voxels of 50 x 50 x 0.1 km, built by inverting gravimetric data over the 6{\deg} x 4{\deg} area centered at the Jiangmen Underground Neutrino Observatory (JUNO) experiment, currently under construction in the Guangdong Province (China). The a-priori modeling is based on the adoption of deep seismic sounding profiles, receiver functions, teleseismic P-wave velocity models and Moho depth maps, according to their own accuracy and spatial resolution. The inversion method allowed for integrating GOCE data with the a-priori information and regularization conditions through a Bayesian approach and a stochastic optimization. GIGJ fits the homogeneously distributed GOCE gravity data, characterized by high accuracy, with a ~1 mGal standard deviation of the residuals, compatible with the observation accuracy. Conversely to existing global models, GIGJ provides a site-specific subdivision of the crustal layers masses which uncertainties include estimation errors, associated to the gravimetric solution, and systematic uncertainties, related to the adoption of a fixed sedimentary layer. A consequence of this local rearrangement of the crustal layer thicknesses is a ~21% reduction and a ~24% increase of the middle and lower crust expected geoneutrino signal, respectively. Finally, the geophysical uncertainties of geoneutrino signals at JUNO produced by unitary uranium and thorium abundances distributed in the upper, middle and lower crust are reduced by 77%, 55% and 78%, respectively. The numerical model is available at http://www.fe.infn.it/u/radioactivity/GIGJ
year | journal | country | edition | language |
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2019-01-23 |