Recent studies have illustrated that the Multichannel Analysis of Surface Waves (MASW) method is an effective geoacoustic parameter inversion tool. This particular tool employs the dispersion property of broadband Scholte-type surface wave signals, which propagate along the interface between the sea water and seafloor. It is of critical importance to establish the theoretical Scholte wave dispersion curve computation model. In this typical study, the stiffness matrix method is introduced to compute the phase speed of the Scholte wave in a layered ocean environment with an elastic bottom. By computing the phase velocity in environments with a typical complexly varying seabed, it is observed that the coupling phenomenon occurs among Scholte waves corresponding to the fundamental mode and the first higher-order mode for the model with a low shear-velocity layer. Afterwards, few differences are highlighted, which should be taken into consideration while applying the MASW method in the seabed. Finally, based on the ingeniously developed nonlinear Bayesian inversion theory, the seafloor shear wave velocity profile in the southern Yellow Sea of China is inverted by employing multi-order Scholte wave dispersion curves. These inversion results illustrate that the shear wave speed is below 700 m/s in the upper layers of bottom sediments. Due to the alternation of argillaceous layers and sandy layers in the experimental area, there are several low-shear-wave-velocity layers in the inversion profile.

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.

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.

近期南海远程声传播实验数据的处理分析表明在深海不完整声道中声道轴以下存在一种会聚区, 该会聚区相比于海面附近的上反转点会聚区在远距离处具有更高的会聚增益. 本文利用射线简正波理论确定了水中反转型焦散线和海面反射型焦散线位置, 对比发现实验中观测到的深海大深度会聚区位置与水中反转型焦散线位置一致, 证明该会聚区是由大量简正波同相叠加形成的下反转点会聚区, 其在深海声道轴以下的一定深度范围内都具有会聚效应, 研究了该会聚区的形成条件以及声源深度变化对会聚区焦散结构的影响, 对比了远距离处上下反转点会聚区的传播损失以及会聚区宽度, 分析表明第七个下反转点会聚区的会聚增益仍不小于10 dB, 研究了声速垂直结构变化对下反转点会聚区的影响, 理论分析结果与实验数据吻合较好.

In research into various hydrophysical and hydroacoustic wave processes, it is extremely important to know the regularities of their propagation in the sea at decreasing depths, especially in the shelf areas, and also to know the regularities of their transformation into seismoacoustic processes in the earth crust. In the course of the processing and analysis of the experimental data of our complex experiment, in this paper we investigate these regularities. In our experiment, we used a low-frequency hydroacoustic transmitter that generated harmonic oscillations at the frequency of 22 Hz and received hydroacoustic systems with a shore laser strainmeter. It was established that hydroacoustic waves, propagating at the shelf of decreasing depth, transform into seismoacoustic waves at the depth of the sea equal to or less than a half-length of the hydroacoustic wave. A comparison of the results of this work with earlier-obtained results allows us to state that such regularities should be inherent to all hydrophysical and hydroacoustic processes.

Non-Gaussian impulsive noise in marine environments strongly influences the detection of weak spectral lines. However, existing detection algorithms based on the Gaussian noise model are futile under non-Gaussian impulsive noise. Therefore, a deep-learning method called AINP+LR-DRNet is proposed for joint detection and the reconstruction of weak spectral lines. First, non-Gaussian impulsive noise suppression was performed by an impulsive noise preprocessor (AINP). Second, a special detection and reconstruction network (DRNet) was proposed. An end-to-end training application learns to detect and reconstruct weak spectral lines by adding into an adaptive weighted loss function based on dual classification. Finally, a spectral line-detection algorithm based on DRNet (LR-DRNet) was proposed to improve the detection performance. The simulation indicated that the proposed AINP+LR-DRNet can detect and reconstruct weak spectral line features under non-Gaussian impulsive noise, even for a mixed signal-to-noise ratio as low as −26 dB. The performance of the proposed method was validated using experimental data. The proposed AINP+LR-DRNet detects and reconstructs spectral lines under strong background noise and interference with better reliability than other algorithms.