Uncertainty analysis in matched-field geoacoustic inversions

被引：0

作者：

Huang, Chen-Fen ^{[1
]}

Gerstoft, Peter ^{[1
]}

Hodgkiss, William S. ^{[1
]}

机构：

[1] Marine Physical Laboratory, Scripps Institution of Oceanography, San Diego, CA 92093-0238, United States

来源：

Journal of the Acoustical Society of America | 2006年 / 119卷 / 01期

关键词：

Quantifying uncertainty for parameter estimates obtained from matched-field geoacoustic inversions using a Bayesian approach requires estimation of the uncertainties in the data due to ambient noise as well as modeling errors. In this study; the variance parameter of the Gaussian error model; hereafter called error variance; is assumed to describe the data uncertainty. In practice; this parameter is not known a priori; and choosing a particular value is often difficult. Hence; to account for the uncertainty in error variance; several methods are introduced for implementing both the full and empirical Bayesian approaches. A full Bayesian approach that permits uncertainty of the error variance to propagate through the parameter estimation processes is a natural way of incorporating the uncertainty of error variance. Due to the large number of unknown parameters in the full Bayesian uncertainty analysis; an alternative; the empirical Bayesian approach; is developed; in which the posterior distributions of model parameters are conditioned on a point estimate of the error variance. Comparisons between the full and empirical Bayesian inferences of model parameters are presented using both synthetic and experimental data. © 2006 Acoustical Society of America;

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摘要：

Journal article (JA)

引用

页码：197 / 207

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