Bayesian model selection applied to self-noise geoacoustic inversion

Battle, David; Gerstoft, Peter; Hodgkiss, William S.; Kuperman, W. A.; Nielsen, Peter L.

doi:10.1121/1.1785671

Cited by 64 publications

(59 citation statements)

References 42 publications

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“…The results of this reseach are documented in five refered jornal papers [1][2][3][4][5][6] and one conference proceeding [7].…”

Section: Research Summarymentioning

confidence: 99%

“…We have concentrated on demonstrating the feasibility of RFC using an efficient 11-parameter description of the environment. The quality of the inversion was addressed by comparing the field using the estimated parameters to a measured field [1][2][3][4][5][6]. Little has been done to indicate the quality of the solution for each parameter, either with the variance of parameter-estimate or preferably the complete a posteriori distribution.…”

Section: Research Summarymentioning

confidence: 99%

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Bayesian Inversion of Radar Clutter

Gerstoft¹,

Hodgkiss²,

Kuperman³

2003

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations end Reports f0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY)2 SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)Office of This report describes the research performed on behalf of the Office of Naval Research (grant #NOOO 14-03-1-0393) to estimation of refractivity profiles from radar clutter returned. Through simulation and experimental results it is shown that the radar clutter return can be used for extracting refractivity profiles. Of particular interest is the uncertainty in these estimates and it is demonstrated how these parameters can be used for radar performance prediction.

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“…The results of this reseach are documented in five refered jornal papers [1][2][3][4][5][6] and one conference proceeding [7].…”

Section: Research Summarymentioning

confidence: 99%

Section: Research Summarymentioning

confidence: 99%

Bayesian Inversion of Radar Clutter

Gerstoft¹,

Hodgkiss²,

Kuperman³

2003

Self Cite

View full text Add to dashboard Cite

show abstract

“…This is a robust and accurate approach and is recommended for inverse problems with only a few parameters ͑e.g., less than eight parameters͒. However, if the number of parameters is large, Monte Carlo methods of numerical integration 4,7 should be used.…”

Section: P͑m͉d ͒ ͑4͒mentioning

confidence: 99%

“…6,7 Under the Bayesian framework, all Under the Bayesian approach, unless there is absolute certainty regarding the value of , inference of m should be made by integrating out the effect of from the joint posterior probability p͑m , ͉ d͒:…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis in matched-field geoacoustic inversions

Huang

Gerstoft

Hodgkiss

2006

The Journal of the Acoustical Society of America

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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.

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“…The acoustic study of seabed layering structure and composition has relied heavily on active-source techniques, although methods using naturally occurring noise 1,2 and man-made sources of opportunity [3][4][5] have been suggested. From these passive techniques, inversion of wind-driven ambient noise recorded at a vertical linear array (VLA) requires only simple hardware and deployment procedures, it has minimal environmental impact, and its generating mechanism is ubiquitous in the ocean, 1,6,7 making this technique suitable for exploring large geographic areas.…”

Section: Introductionmentioning

confidence: 99%

Trans-dimensional geoacoustic inversion of wind-driven ambient noise

Quijano¹,

Dosso²,

Dettmer³

et al. 2012

The Journal of the Acoustical Society of America

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This letter applies trans-dimensional Bayesian geoacoustic inversion to quantify the uncertainty due to model selection when inverting bottom-loss data derived from wind-driven ambient-noise measurements. A partition model is used to represent the seabed, in which the number of layers, their thicknesses, and acoustic parameters are unknowns to be determined from the data. Exploration of the parameter space is implemented using the Metropolis–Hastings algorithm with parallel tempering, whereas jumps between parameterizations are controlled by a reversible-jump Markov chain Monte Carlo algorithm. Sediment uncertainty profiles from inversion of simulated and experimental data are presented.

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Bayesian model selection applied to self-noise geoacoustic inversion

Cited by 64 publications

References 42 publications

Bayesian Inversion of Radar Clutter

Bayesian Inversion of Radar Clutter

Uncertainty analysis in matched-field geoacoustic inversions

Trans-dimensional geoacoustic inversion of wind-driven ambient noise

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