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A Bayesian approach to matched-field...

University of California, San Diego.

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A Bayesian approach to matched-field geoacoustic inversion with analysis of ASIAEX experimental data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A Bayesian approach to matched-field geoacoustic inversion with analysis of ASIAEX experimental data./
Author:	Huang, Chen-Fen.
Description:	123 p.
Notes:	Chair: William S. Hodgkiss.
Contained By:	Dissertation Abstracts International66-10B.
Subject:	Physical Oceanography. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3191986
ISBN:	9780542371134

A Bayesian approach to matched-field geoacoustic inversion with analysis of ASIAEX experimental data.
Huang, Chen-Fen.

A Bayesian approach to matched-field geoacoustic inversion with analysis of ASIAEX experimental data. - 123 p.

Chair: William S. Hodgkiss.

Thesis (Ph.D.)--University of California, San Diego, 2005.

This dissertation applies a Bayesian framework for making quantitative statistical inferences about geoacoustic properties from ocean acoustic data using matched-field processing techniques. Data acquired during the ASIAEX 2001 East China Sea experiment are used to infer the geoacoustic properties.

ISBN: 9780542371134Subjects--Topical Terms:

1019163
Physical Oceanography.

A Bayesian approach to matched-field geoacoustic inversion with analysis of ASIAEX experimental data.
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020 $a 9780542371134
035 $a (UMI)AAI3191986
035 $a AAI3191986
040 $a UMI $c UMI
100 1 $a Huang, Chen-Fen. $3 1025163
245 1 2 $a A Bayesian approach to matched-field geoacoustic inversion with analysis of ASIAEX experimental data.
300 $a 123 p.
500 $a Chair: William S. Hodgkiss.
500 $a Source: Dissertation Abstracts International, Volume: 66-10, Section: B, page: 5294.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2005.
520 $a This dissertation applies a Bayesian framework for making quantitative statistical inferences about geoacoustic properties from ocean acoustic data using matched-field processing techniques. Data acquired during the ASIAEX 2001 East China Sea experiment are used to infer the geoacoustic properties.
520 $a In a Bayesian approach, information and uncertainty regarding model parameters obtained from the measurements are summarized in the posterior probability distribution. This posterior distribution is proportional to the product of a prior distribution (which incorporates information on model parameters before the measurements) and of a likelihood function (which quantifies how well a model fits the measurements). From this posterior distribution of model parameters, we obtain all information about the model parameters, such as maximum a posteriori estimate (best-fit model), mean as well as standard deviation.
520 $a The quality of the best-fit model is checked using matched-field processing for source localization. In the less than 1 kHz frequency band, the effect of environmental mismatch on source tracking can be reduced by using inversion techniques to estimate geoacoustic parameters, resulting in improved source localization performance. The parameter uncertainty (in terms of mean and standard deviation) given by the Bayesian approach is validated by comparing the variabilities of the estimated parameters inverted from multiple independent data sets.
520 $a A Bayesian approach to inverse problems requires estimation of the uncertainties in the data. An extension of the Bayesian parameter uncertainty analysis to include the uncertainty of data errors is carried out. Following a full Bayesian methodology, we derive the analytic expressions for the posterior probability distribution of the model parameters for both single and multi-frequency data.
520 $a The impact of uncertainty embedded in the geoacoustic inversion results on the estimation of transmission loss is investigated. An approach for estimating the statistical properties of transmission loss is developed using information on the model parameters obtained from the inversion. The utility of this approach is that one can compute the probability distributions of transmission loss at all frequencies, ranges and depths. Examples demonstrate the use of transmission loss probability density functions to extract characteristic features such as median and lower/upper percentiles.
590 $a School code: 0033.
650 4 $a Physical Oceanography. $3 1019163
650 4 $a Physics, Acoustics. $3 1019086
690 $a 0415
690 $a 0986
710 2 $a University of California, San Diego. $3 1018093
773 0 $t Dissertation Abstracts International $g 66-10B.
790 $a 0033
790 1 0 $a Hodgkiss, William S., $e advisor
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3191986