Geoacoustic Inversion Using an Autonomous Underwater Vehicle in Conjunction With Distributed Sensors

Zhang, Zheng; Yang, T. C.; Pan, Xiaoyong

doi:10.1109/joe.2018.2869481

Cited by 8 publications

(2 citation statements)

References 39 publications

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“…Matched field processing (MFP) is a well-known technology for solving inversion problems by comparing acoustic data with solutions to wave equations [1]. Depending on the unknown quantity, MFP can be divided into source localization [2], tomography [3], and geoacoustic inversion [4,5]. Owing to the temporal and spatial variations in environmental parameters in the ocean and difficulties of marine measurement, there is a mismatch between the ocean and its environmental model, which is a challenge for MFP.…”

Section: Introductionmentioning

confidence: 99%

A simplified model for acoustic focalization in environments with seabed uncertainties

Ou²,

Huang³

et al. 2023

Front. Phys.

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Introduction: Parameter mismatch poses a challenge to source localization in cases involving environments with seabed uncertainties. By including environmental parameters in the search space, focalization can be used to estimate the location of the source using environmental information that is limited a priori. Methods: To reduce the number of parameters, a simplified seabed model is proposed here for such focalization. Only two geoacoustic parameters—the amplitude F and phase cF of reflection—are used to describe the seabed. Focalization is generally tested using genetic algorithms for the colored noise case (COLNOISE) benchmark problem. Results: The proposed simplified model can obtain the location of the source more easily than a layered model. Due to its advantage in terms of parameter sensitivity and inter-coupling, the simplified model can ensure the robustness of the results of inversion. The proposed method was tested on a broadband signal in the Asian Seas International Acoustics Experiment (ASIAEX2001), where both the location and the geoacoustic parameters were easily inverted. Discussion: The simplified model provides a sufficiently high acoustic resolution for focalization, and its reduction of the geoacoustic parameters helpes solve the problem of inversion.

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Section: Introductionmentioning

confidence: 99%

A simplified model for acoustic focalization in environments with seabed uncertainties

Ou²,

Huang³

et al. 2023

Front. Phys.

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“…But in practical application, a VLA or HLA carries a high computational cost, and the accuracy of the measured data can be affected greatly by the array shape, which is influenced by ocean currents and storms [9]. Therefore, more and more geoacoustic inversion based on low-frequency sound propagation characteristics have been proposed, and these methods greatly reduce the cost and complexity for recording systems in measurement [10][11][12][13]. However, the sea bottom was still set like a liquid medium.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inversion for Geoacoustic Parameters in Shallow Sea

Zheng

Zhu

Wang

et al. 2020

Sensors

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Geoacoustic parameter inversion is a crucial issue in underwater acoustic research for shallow sea environments and has increasingly become popular in the recent past. This paper investigates the geoacoustic parameters in a shallow sea environment using a single-receiver geoacoustic inversion method based on Bayesian theory. In this context, the seabed is regarded as an elastic medium, the acoustic pressure at different positions under low-frequency is chosen as the study object, and the theoretical prediction value of the acoustic pressure is described by the Fast Field Method (FFM). The cost function between the measured and modeled acoustic fields is established under the assumption of Gaussian data errors using Bayesian methodology. The Bayesian inversion method enables the inference of the seabed geoacoustic parameters from the experimental data, including the optimal estimates of these parameters, such as density, sound speed and sound speed attenuation, and quantitative uncertainty estimates. The optimization is carried out by simulated annealing (SA), and the Posterior Probability Density (PPD) is given as the inversion result based on the Gibbs Sampler (GS) algorithm. Inversion results of the experimental data are in good agreement with both measured values and estimates from Genetic Algorithm (GA) inversion result in the same environment. Furthermore, the results also indicate that the sound speed and density in the seabed have fewer uncertainties and are more sensitive to acoustic pressure than the sound speed attenuation. The sea noise could increase the variance of PPD, which has less influence on the sensitive parameters. The mean value of PPD could still reflect the true values of geoacoustic parameters in simulation.

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Reciprocity-based Synthetic Aperture Geoacoustic Inversion Using a Moving Source in the Presence of Doppler Effect

Shen

Pan

2020

Global Oceans 2020: Singapore – U.S. Gulf Coast

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Geoacoustic Inversion Using an Autonomous Underwater Vehicle in Conjunction With Distributed Sensors

Cited by 8 publications

References 39 publications

A simplified model for acoustic focalization in environments with seabed uncertainties

A simplified model for acoustic focalization in environments with seabed uncertainties

Bayesian Inversion for Geoacoustic Parameters in Shallow Sea

Reciprocity-based Synthetic Aperture Geoacoustic Inversion Using a Moving Source in the Presence of Doppler Effect

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