GIGJ: A Crustal Gravity Model of the Guangdong Province for Predicting the Geoneutrino Signal at the JUNO Experiment
Articolo
Data di Pubblicazione:
2019
Citazione:
GIGJ: A Crustal Gravity Model of the Guangdong Province for Predicting the Geoneutrino Signal at the JUNO Experiment / M. Reguzzoni, L. Rossi, M. Baldoncini, I. Callegari, P. Poli, D. Sampietro, V. Strati, F. Mantovani, G. Andronico, V. Antonelli, M. Bellato, E. Bernieri, A. Brigatti, R. Brugnera, A. Budano, M. Buscemi, S. Bussino, R. Caruso, D. Chiesa, D. Corti, F. Dal Corso, X.F. Ding, S. Dusini, A. Fabbri, G. Fiorentini, R. Ford, A. Formozov, G. Galet, A. Garfagnini, M. Giammarchi, A. Giaz, M. Grassi, A. Insolia, R. Isocrate, I. Lippi, F. Longhitano, D. Lo Presti, P. Lombardi, Y. Malyshkin, F. Marini, S.M. Mari, C. Martellini, E. Meroni, M. Mezzetto, L. Miramonti, S. Monforte, M. Montuschi, M. Nastasi, F. Ortica, A. Paoloni, S. Parmeggiano, D. Pedretti, N. Pelliccia, R. Pompilio, E. Previtali, G. Ranucci, A.C. Re, B. Ricci, A. Romani, P. Saggese, G. Salamanna, F.H. Sawy, G. Settanta, M. Sisti, C. Sirignano, M. Spinetti, L. Stanco, G. Verde, L. Votano. - In: JOURNAL OF GEOPHYSICAL RESEARCH. SOLID EARTH. - ISSN 2169-9313. - 142:4(2019 Apr 29), pp. 4231-4249.
Abstract:
Gravimetric methods are expected to play a decisive role in geophysical modeling of theregional crustal structure applied to geoneutrino studies. GIGJ (GOCE Inversion for Geoneutrinos atJUNO) is a 3‐D numerical model constituted by ~46 × 103voxels of 50 × 50 × 0.1 km, built by invertingGOCE (Gravityfield and steady‐state Ocean Circulation Explorer) gravimetric data over the 6° × 4° areacentered at the JUNO (Jiangmen Underground Neutrino Observatory) experiment, currently underconstruction in the Guangdong Province (China). The a priori modeling is based on the adoption of deepseismic sounding profiles, receiver functions, teleseismicPwave velocity models, and Moho depth maps,according to their own accuracy and spatial resolution. The inversion method allowed for integrating GOCEdata with the a priori information and some regularization conditions through a Bayesian approach and astochastic optimization. GIGJfits the highly accurate and homogeneously distributed GOCE gravity datawith a ~1 mGal standard deviation of the residuals, compatible with the observation accuracy. GIGJ providesa 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 afixedsedimentary 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. Thegeophysical uncertainties of geoneutrino signals at JUNO produced by unitary uranium and thoriumabundances 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).
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
gravimetric methods; geoneutrino studies; Bayesian method; upper; middle; and lower crust; South China Block; GOCE data gravimetric inversion; geophysical uncertainties; Monte Carlo stochastic optimization
Elenco autori:
M. Reguzzoni, L. Rossi, M. Baldoncini, I. Callegari, P. Poli, D. Sampietro, V. Strati, F. Mantovani, G. Andronico, V. Antonelli, M. Bellato, E. Bernieri, A. Brigatti, R. Brugnera, A. Budano, M. Buscemi, S. Bussino, R. Caruso, D. Chiesa, D. Corti, F. Dal Corso, X.F. Ding, S. Dusini, A. Fabbri, G. Fiorentini, R. Ford, A. Formozov, G. Galet, A. Garfagnini, M. Giammarchi, A. Giaz, M. Grassi, A. Insolia, R. Isocrate, I. Lippi, F. Longhitano, D. Lo Presti, P. Lombardi, Y. Malyshkin, F. Marini, S.M. Mari, C. Martellini, E. Meroni, M. Mezzetto, L. Miramonti, S. Monforte, M. Montuschi, M. Nastasi, F. Ortica, A. Paoloni, S. Parmeggiano, D. Pedretti, N. Pelliccia, R. Pompilio, E. Previtali, G. Ranucci, A.C. Re, B. Ricci, A. Romani, P. Saggese, G. Salamanna, F.H. Sawy, G. Settanta, M. Sisti, C. Sirignano, M. Spinetti, L. Stanco, G. Verde, L. Votano
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