GIGJ: A Crustal Gravity Model of the Guangdong Province for Predicting the Geoneutrino Signal at the JUNO Experiment. Issue 4 (29th April 2019)

• Record Type: Journal Article
• Authors: Reguzzoni, M.; Rossi, L.; Baldoncini, M.; Callegari, I.; Poli, P.; Sampietro, D.; Strati, V.; Mantovani, F.; Andronico, G.; Antonelli, V.; Bellato, M.; Bernieri, E.; Brigatti, A.; Brugnera, R.; Budano, A.; Buscemi, M.; Bussino, S.; Caruso, R.; Chiesa, D.; Corti, D.; Dal Corso, F.; Ding, X.F.; Dusini, S.; Fabbri, A.; Fiorentini, G.; Ford, R.; Formozov, A.; Galet, G.; Garfagnini, A.; Giammarchi, M.; Giaz, A.; Grassi, M.; Insolia, A.; Isocrate, R.; Lippi, I.; Longhitano, F.; Lo Presti, D.; Lombardi, P.; Malyshkin, Y.; Marini, F.; Mari, S.M.; Martellini, C.; Meroni, E.; Mezzetto, M.; Miramonti, L.; Monforte, S.; Montuschi, M.; Nastasi, M.; Ortica, F.; Paoloni, A.; Parmeggiano, S.; Pedretti, D.; Pelliccia, N.; Pompilio, R.; Previtali, E.; Ranucci, G.; Re, A.C.; Ricci, B.; Romani, A.; Saggese, P.; Salamanna, G.; Sawy, F.H.; Settanta, G.; Sisti, M.; Sirignano, C.; Spinetti, M.; Stanco, L.; Verde, G.; Votano, L.;
• Is Part Of: Journal of geophysical research. Volume 124:Issue 4(2019)
• Page Start: 4231
• Page End: 4249
• Publication Date: 2019-04-29
• Subjects: Bayesian method -- upper, middle, and lower crust -- South China Block -- GOCE data gravimetric inversion -- geophysical uncertainties -- Monte Carlo stochastic optimization; Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1029/2018JB016681
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: 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 3‐D numerical model constituted by ~46 × 10 3 voxels of 50 × 50 × 0.1 km, built by inverting GOCE (Gravity field and steady‐state Ocean Circulation Explorer) gravimetric data over the 6° × 4° area centered at the JUNO (Jiangmen Underground Neutrino Observatory) 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 some regularization conditions through a Bayesian approach and a stochastic optimization. GIGJ fits the highly accurate and homogeneously distributed GOCE gravity data with a ~1 mGal standard deviation of the residuals, compatible with the observation accuracy. GIGJ provides a site‐specific subdivision of the crustal layers masses, of 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 geoneutrino signal, respectively. 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 this site (http://www.fe.infn.it/radioactivity/GIGJ ). Key Points: A gravity‐based 3‐D crustal model beneath the Guangdong province (China) was built to predict the geoneutrino signal at the JUNO experiment The adopted Bayesian method allows for fitting gravimetric observations integrating local prior distribution with regularization conditions GIGJ fitted GOCE gravity data with a ~1 mGal standard deviation of the residuals, compatible with the observation accuracy