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• W2181984765 abstract "Magnetotelluric directional analysis and impedance tensor decomposition are basic tools that are being standardly employed to validate a local/regional composite electrical model of the underlying structure as well as to extract quantitative information about both the regional c onductor, often with a specific type of symmetry, and the local distorters. As the effect of local galvanic distor tions can result in considerably blurring the image of the deeper regional conductor, reliable quantitative estimat es of both the decomposition parameters and their uncertainties are needed. Bayesian stochastic methods are parti cularly suitable for this purpose, as they approach the problem of the parameter estimation and their uncertainty characterization in a fully probabilistic fashion, through the use of posterior model probabilities, rather than by der iving single point estimates of the model parameters and assessing their uncertainties via a linearized covaria nce projection from the data space into the model domain. We use the standard Groom-Bailey 3-D local/2-D regional composite model in our bayesian approach to the magnetotelluric decomposition. We assume that the experimental impedance estimates are contamined with the Gaussian noise and define the likelihood of a particular comp osite model with respect to the observed data in terms of the least-squares misfit between the model and exper imental impedances. We use non-informative, flat priors over physically reasonable intervals for the standa rd Groom-Bailey decomposition parameters. Further, we apply two numerical variants of a Monte Carlo technique, specifically the Markov chain Monte Carlo procedure based on the Gibbs sampler and a single-component adaptive Metropolis algorithm, to simulate samples from the posterior distribution of the composite models conditi oned on the experimental data. From the posterior samples, we characterize the estimates and uncertainties of the individual decomposition parameters by using the respective marginal posterior probabilities. By analy zing results of our stochastic decomposition experiments carried out with several recently published impedance data sets, both synthetic and practical, we can conclude that the stochastic scheme performs reliably for a variety of models, including the multisite and multifrequency case with up to several hundreds of parameters. Though the Monte Carlo samplers are computationally very intensive, the recent adaptive Metropolis algorithm seems to efficient ly increase the speed of the simulations for large-scale problems." @default.
• W2181984765 created "2016-06-24" @default.
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• W2181984765 date "2005-01-01" @default.
• W2181984765 modified "2023-09-27" @default.
• W2181984765 title "Bayesian Monte Carlo for MT Tensor Decomposition" @default.
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