Guo, Rongwen; Dosso, S.E.; Liu, Jianxin; Dettmer, Jan; Tong, Xiaozhong
This paper applies a Bayesian approach to examine non-linearity for the 1-D magnetotelluric (MT) inverse problem. In a Bayesian formulation the posterior probability density (PPD), which combines data and prior information, is interpreted in terms of parameter estimates and uncertainties, which requires optimizing and integrating the PPD. Much work on 1-D MT inversion has been based on (approximate) linearized solutions, but more recently fully non-linear (numerical) approaches have been...[Show more]
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