Bayesian Inversion for Geoacoustic Parameters in Shallow Sea

Zheng, Guangxue; Zhu, Hanhao; Wang, Xiaohan; Khan, Sartaj; Li, Nansong; Xue, Yangyang

doi:10.3390/s20072150

Cited by 13 publications

(7 citation statements)

References 26 publications

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“…The sound pressure TL calculation formula is given by Equation ( 13 In Figure 3, the dotted lines are calculated results by FEM, and the solid lines are the results by three comparison acoustic field simulation methods. Based on their applicability, the fast field method (Scotter program) [17,25,26] and the parabolic equation method (RAM program) [26,27] are used to examine the error of FEM for the models in Figure 2ac, respectively. The comparison results in Figure 3 show that the TL curves that were simulated by FEM are highly consistent with the results obtained by the existing simulation programs.…”

Section: Comparison Of Simulation Resultsmentioning

confidence: 99%

Characteristics of Very Low Frequency Sound Propagation in Full Waveguides of Shallow Water

Zhu

Wang

et al. 2020

Sensors

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This work is concerned with the characteristics of very low frequency sound propagation (VLF, ≤100 Hz) in the shallow marine environment. Under these conditions, the classical hypothesis of considering the sea bottom as a fluid environment is no longer appropriate, and the sound propagation characteristics at the sea bottom should be also considered. Hence, based on the finite element method (FEM), and setting the sea bottom as an elastic medium, a proposed model which unifies the sea water and sea bottom is established, and the propagation characteristics in full waveguides of shallow water can be synchronously discussed. Using this model, the effects of the sea bottom topography and the various geoacoustic parameters on VLF sound propagation and its corresponding mechanisms are investigated through numerical examples and acoustic theory. The simulation results demonstrate the adaptability of the proposed model to complex shallow water waveguides and the accuracy of the calculated acoustic field. For the sea bottom topography, the greater the inclination angle of an up-sloping sea bottom, the stronger the leak of acoustic energy to the sea bottom, and the more rapid the attenuation of the acoustic energy in sea water. The effect of a down-sloping sea bottom on acoustic energy is the opposite. Moreover, the greater the pressure wave (P-wave) speed in the sea bottom, the more acoustic energy remains in the water rather than leaking into the bottom; the influence laws of the density and the shear wave (S-wave) speed in the sea bottom are opposite.

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Section: Comparison Of Simulation Resultsmentioning

confidence: 99%

Characteristics of Very Low Frequency Sound Propagation in Full Waveguides of Shallow Water

Zhu

Wang

et al. 2020

Sensors

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“…In this model, the input layer receives simulated sound pressure data, denoted as "p," which includes a set of sound pressure data p j (r i , z i ), where the range of i extends from 1 to I. The number of neurons in the hidden layer is set at 9, and this choice is influenced by various factors, one of which is the empirical rule for parameter a, which is set to a = -15 in this context (Zheng et al, 2021). Furthermore, the number of neurons in the output layer, represented as "l," is determined based on the number of geophysical parameters that need to be inverted.…”

Section: Bpnn Inversion Model Of Geoacoustic Parameters Inversionmentioning

confidence: 99%

A back propagation neural network-based approach for inverting layered seabed acoustic parameters in shallow waters

Wang,

Cui,

Zhu

et al. 2024

Front. Mar. Sci.

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IntroductionExisting methods primarily focus on earth acoustic parameters inversion under specific layered structures. However, they face challenges with experimental data from unknown seabed stratification, hindering accurate parameter inversion.MethodsTo address this, a novel algorithm combines Back Propagation Neural Network (BPNN) for distinguishing seabed stratification and inverting acoustic parameters. Simulated sound pressure data disturb seabed parameters as input, enabling feature recognition for training the neural network inversion model. Acoustic parameters are then estimated under identified stratification using the sound field model.ResultsThe inversion model is validated using simulation and pool shrinkage data. Results show the neural network model effectively stratifies simulation and experimental data, providing accurate inversion results for acoustic parameters corresponding to distinct layers.DiscussionThe neural network model's accuracy and practicality are confirmed through hierarchical judgment of scale test data and acoustic parameter inversion. This approach introduces a new perspective for shallow sea acoustic parameter inversion, offering a promising application scenario.

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“…Although the MHS results do not depend on the initial model, the accuracy of the inversion results can be improved by selecting the correct initial value. In this study, the MAP estimate searched by the OSA was used as the initial model for the MHS (Zheng et al, 2020).…”

Section: Nonlinear Bayesian Inversion Theorymentioning

confidence: 99%

Bayesian geoacoustic parameters inversion for multi-layer seabed in shallow sea using underwater acoustic field

Xue

Zhu²,

Wang³

et al. 2023

Front. Mar. Sci.

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Seabed geoacoustic parameters play an important role in underwater acoustic channel modeling. Traditional methods to determine these parameters, for example, drilling, are expensive and are being replaced by acoustic inverse technology. An inversion method based on Bayesian theory is presented to derive the structure and geoacoustic parameters of a layered seabed in a shallow sea. The seabed was considered a layered elastic medium. The objective of this research was to use the sound pressure detected by underwater acoustic sensors at different positions and to use nonlinear Bayesian inversion to estimate the geoacoustic parameters and their uncertainties in the multi-layer seabed. Specifically, the thickness, density, compression wave speed, shear wave speed, and the attenuation of these two wave speeds were determined. The maximum a posterior (MAP) model and posterior probability distribution of each parameter were estimated using the optimized simulated annealing (OSA) and Metropolis-Hastings sampling (MHS) methods. Model selection was carried out using the Bayesian information criterion (BIC) to determine the optimal model that thoroughly explained the experimental data for different parameterizations. The results showed that the OSA is much more capable of delivering high-accuracy results in multi-layer seabed models. The compression wave speed and shear wave speed were less uncertain than the other parameters, and the parameters in the upper layer had less uncertainty than those in the lower layer.

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Bayesian Inversion for Geoacoustic Parameters in Shallow Sea

Cited by 13 publications

References 26 publications

Characteristics of Very Low Frequency Sound Propagation in Full Waveguides of Shallow Water

Characteristics of Very Low Frequency Sound Propagation in Full Waveguides of Shallow Water

A back propagation neural network-based approach for inverting layered seabed acoustic parameters in shallow waters

Bayesian geoacoustic parameters inversion for multi-layer seabed in shallow sea using underwater acoustic field

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