Parameter estimation using multifrequency range-dependent acoustic data in shallow water

Gerstoft, Peter; Gingras, D.F.

doi:10.1121/1.414818

Cited by 78 publications

(54 citation statements)

References 15 publications

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“…[1][2][3][4] Although the goal of inversions is to infer the geoacoustic properties of the sea floor based on acoustic field observations received on an array, uncertainty resulting from temporal and spatial variability of the ocean sound speed plays an import role in the estimation of geoacoustic parameters, especially for higher frequencies.…”

Section: Introductionmentioning

confidence: 99%

Effect of ocean sound speed uncertainty on matched-field geoacoustic inversion

Huang

Gerstoft

Hodgkiss

2008

The Journal of the Acoustical Society of America

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The effect of ocean sound speed uncertainty on matched-field geoacoustic inversion is investigated using data from the SW06 experiment along a nearly range-independent bathymetric track. Significant sound speed differences were observed at the source and receiving array and several environmental parameterizations were investigated for the inversion including representing the ocean sound speed at both source and receivers with empirical orthogonal function (EOF) coefficients. A genetic algorithm-based global optimization method was applied to the candidate environmental models. Then, a Bayesian inversion technique was used to quantify uncertainty in the environmental parameters for the best environmental model, which included an EOF description of the ocean sound speed.

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Section: Introductionmentioning

confidence: 99%

Effect of ocean sound speed uncertainty on matched-field geoacoustic inversion

Huang

Gerstoft

Hodgkiss

2008

The Journal of the Acoustical Society of America

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“…From the environmental viewpoint, this implies that, for a given acoustic data set and the corresponding real environment, the simulated acoustics closest to the acoustic data has to be parameterized by an environment slightly shifted from the real environment and here designated as the 'acoustically equivalent environment'. This fact has been verified with model-based acoustic inversion processors [2], when comparing e.g. inverted temperature profiles with those measured by a conductivity-temperature-depth profiler (CTD).…”

Section: Introductionmentioning

confidence: 81%

Bayesian acoustic prediction assimilating oceanographic and acoustically inverted data

Martins

Jesus

2009

Journal of Marine Systems

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The prediction of the acoustic field evolution on a day to week frame, in a given oceanic area, is an important issue in sonar performance modeling. It relies primarily on acoustic propagation models, which convert water column and geometric/geoacoustic parameters to 'instantaneous' acoustic field estimates. In practice, to model the acoustic field, even the most accurate acoustic models have to be fed with simplified environmental descriptions, due to computational issues and to a limited knowledge of the environment. This is a limitation, for example, in acoustic inversion methods, in which, by maximizing the proximity between measured and modeled acoustic signals, the estimated environmental parameters are deviated from reality, forming what is normally called an 'acoustically equivalent environment'. This problem arises also in standard acoustic prediction, in which, the oceanographic forecasts and bottom data (typically from archives) are fed directly to an acoustic model. The claim in the present work is that, by converting the oceanographic prediction and the bottom properties to 'acoustically equivalent' counterparts, the acoustic prediction can be obtained in an optimal way, adapted to the environmental model at hand. Here, acoustic prediction is formulated as a Bayesian estimation problem, in which, the observables are oceanographic forecasts, a set of known bottom parameters, a set of acoustic data, and a set of water column data. The predictive posterior PDF of the future acoustic signal is written as a function of elementary PDF functions relating these observables and 'acoustically equivalent' environmental parameters. The latter are obtained by inversion of acoustic data. The concept is tested on simulated data based on water column measurements and forecasts for the MREA'03 sea trial.

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“…The GA has been successfully applied to many of these problems, including for example geo-acoustic inversion in underwater acoustics (Gerstoft 1994, Gerstoft andGingras 1996). The algorithm is loosely based on the process of natural selection in evolutionary biology.…”

Section: The Genetic Algorithm Techniquementioning

confidence: 99%

Validation of genetic algorithm-based optimal sampling for ocean data assimilation

et al. 2016

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Abstract-Regional ocean models are capable of forecasting conditions for usefully long intervals of time (days) provided that initial and ongoing conditions can be measured. In resource-limited circumstances, the placement of sensors in optimal locations is essential. Here, a nonlinear optimization approach to determine optimal adaptive sampling that uses the Genetic Algorithm (GA) method is presented. The method determines sampling strategies that minimize a user-defined physics-based cost function. The method is evaluated using identical twin experiments, comparing hindcasts from an ensemble of simulations that assimilate data selected using the GA adaptive sampling and other methods. For skill metrics, we employ the reduction of the ensemble root-mean-square-error (RMSE) between the "true" data-assimilative ocean simulation and the different ensembles of data-assimilative hindcasts. A 5-glider optimal sampling study is set up for a 400 km x 400 km domain in the Middle Atlantic Bight region, along the New Jersey shelf-break. Results are compared for several ocean and atmospheric forcing conditions. Index Terms-Genetic algorithms, ocean technology, optimization methods sampling methods, adaptive sampling, computational ocean modeling, data assimilation, error subspace statistical estimation, OSSE

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Parameter estimation using multifrequency range-dependent acoustic data in shallow water

Cited by 78 publications

References 15 publications

Effect of ocean sound speed uncertainty on matched-field geoacoustic inversion

Effect of ocean sound speed uncertainty on matched-field geoacoustic inversion

Bayesian acoustic prediction assimilating oceanographic and acoustically inverted data

Validation of genetic algorithm-based optimal sampling for ocean data assimilation

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