{"refrec":{"BRefID":332970,"RR":"<b>Fregoso, E.; Palafox, A.; Moreles, M.A.</b> (2019). Initializing cross-gradients joint inversion of gravity and magnetic data with a Bayesian surrogate gravity model. <i>Pure Appl. Geophys. 177(2)</i>: 1029-1041. <a href=\"https://dx.doi.org/10.1007/s00024-019-02334-w\" target=\"_blank\">https://dx.doi.org/10.1007/s00024-019-02334-w</a>","BEntID":326582,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>Pure Appl. Geophys. 177(2)</i>: 1029-1041. <a href=\"https://dx.doi.org/10.1007/s00024-019-02334-w\" target=\"_blank\">https://dx.doi.org/10.1007/s00024-019-02334-w</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Fregoso, E.; Palafox, A.; Moreles, M.A.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Fregoso, E.; Palafox, A.; Moreles, M.A.","Englishabstract":"Cross-gradients joint inversion of gravity and magnetic data is the focus of this work. Cross-gradients are introduced as a constraint in the minimization of a least square functional including the misfits of the available data. We propose to initialize the cross gradients iterations with a surrogate density model. The latter is constructed by means of Bayesian estimation in a low dimensional parameter space. To sample from the posterior, an affine invariant MCMC is also introduced. 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