comparison between Gauss-Newton and Markov-chain onte Carlo–based methods for inverting spectral nduced-polarization data for Cole-Cole parameters
نویسندگان
چکیده
We have developed a Bayesian model to invert spectral induced-polarization SIP data for Cole-Cole parameters using Markov-chain Monte Carlo MCMC sampling methods. We compared the performance of the MCMC-based stochastic method with an iterative Gauss-Newton-based deterministic method for Cole-Cole parameter estimation through inversion of synthetic and laboratory SIP data. The Gauss-Newton-based method can provide an optimal solution for given objective functions under constraints, but the obtained optimal solution generally depends on the choice of initial values and the estimated uncertainty information often is inaccurate or insufficient. In contrast, the MCMC-based inversion method provides extensive global
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