Experimental study of geo-acoustic inversion uncertainty due to ocean sound-speed fluctuations

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.

Section: Introductionmentioning

confidence: 99%

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

Huang

Gerstoft

Hodgkiss

2008

“…The data was simulated using the C-SNAP model 20 in a 80 m deep range-independent shallow water scenario similar to that of the ADVENT'99 experiment. 21 The acoustic source is placed at 76 m depth and at 5 km range from a 32-sensors vertical array. The source is emitting a series of multitones between 300 and 600 Hz with 100 Hz increment.…”

Section: Simulation Resultsmentioning

confidence: 99%

Broadband matched-field processing: Coherent and incoherent approaches

Soares

Jesus

2003

Matched-field based methods always involve the comparison of the output of a physical model and the actual data. The method of comparison and the nature of the data varies according to the problem at hand, but the result becomes always largely conditioned by the accurateness of the physical model and the amount of data available. The usage of broadband methods has become a widely used approach to increase the amount of data and to stabilize the estimation process. Due to the difficulties to accurately predict the phase of the acoustic field the problem whether the information should be coherently or incoherently combined across frequency has been an open debate in the last years. This paper provides a data consistent model for the observed signal, formed by a deterministic channel structure multiplied by a perturbation random factor plus noise. The cross-frequency channel structure and the decorrelation of the perturbation random factor are shown to be the main causes of processor performance degradation. Different Bartlett processors, such as the incoherent processor ͓Baggeroer et al., J. Acoust. Soc. Am. 80, 571-587 ͑1988͔͒, the coherent normalized processor ͓Z.-H. Michalopoulou, IEEE J. Ocean Eng. 21, 384 -392 ͑1996͔͒ and the matched-phase processor ͓Orris et al., J. Acoust. Soc. Am. 107, 2563-2375 ͑2000͔͒, are reviewed and compared to the proposed cross-frequency incoherent processor. It is analytically shown that the proposed processor has the same performance as the matched-phase processor at the maximum of the ambiguity surface, without the need for estimating the phase terms and thus having an extremely low computational cost.

“…[2][3][4] This reduction in resolution causes SSP uncertainty, especially when internal waves or currents generate strong, temporally varying SSP anomalies. [5][6][7] This uncertainty can severely effect the accuracy of inversion for other parameters. 6,7 We show that SSPs in range-independent shallow ocean environments can be resolved using compressive sensing 8,9 (CS).…”

Section: Introductionmentioning

confidence: 99%

Compressive acoustic sound speed profile estimation

Bianco

Gerstoft

2016

Ocean acoustic sound speed profile (SSP) estimation requires the inversion of acoustic fields using limited observations. Compressive sensing (CS) asserts that certain underdetermined problems can be solved in high resolution, provided their solutions are sparse. Here, CS is used to estimate SSPs in a range-independent shallow ocean by inverting a non-linear acoustic propagation model. It is shown that SSPs can be estimated using CS to resolve fine-scale structure.