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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页码：197 / 207
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