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The acoustic properties of seafloor sediments on continental slopes play a crucial role in underwater acoustic propagation, communication, and detection. To investigate the acoustic characteristics and spatial distribution patterns of sediments on the continental slope, a geoacoustic experiment was conducted in the northwestern South China Sea. The experiment covered two sections: one crossing the shelf and slope in the downslope direction, and the other near the shelf break in the along-slope direction. In situ techniques, sediment sampling, and laboratory measurements were used to acquire data on sediment acoustic properties (such as sound speed and attenuation) and physical properties (including particle composition, density, porosity, and mean grain size). The experimental findings revealed several key points: (1) Acoustic properties of shallow water coarse-grained sediments and deep-sea sediments were higher when measured in the laboratory compared to in situ measurements. (2) Relationships between measured attenuation and physical properties, as well as between sound speed and mean grain size, showed deviations from previous empirical equations. (3) Sediment acoustic and physical properties exhibited significant variations in the downslope direction, while showing gradual variations in the along-slope direction. These variations can be attributed to sedimentary environmental factors such as material sources, hydrodynamic conditions, and water depth. [ABSTRACT FROM AUTHOR]

This study focuses on the inversion of sub-bottom profile (SBP) data in the northern South China Sea using an empirical relationship derived from sediment acoustic data. The sub-bottom profile is primarily utilized for various marine applications, such as geological mapping and resource exploration. In this research, we present a study conducted in the northern slope canyon of the South China Sea. Firstly, we obtained the seabed reflection coefficient from sub-bottom profiles obtained by the autonomous underwater vehicle (AUV) detection system. Secondly, we utilized the acoustic empirical relationship in the northern South China Sea to establish relationship equations between the seabed reflection coefficient and the porosity, density, and average particle size of the sediment at a main frequency of 4 kHz (the AUV shallow profile main frequency). Then, using these equations, we were able to invert the physical parameters such as porosity, density, and average particle size of the seabed surface sediments. Finally, the inverted results are compared and analyzed by using the sediment samples test data. The overall deviation rate of the inverted physical parameters is within the range of ±10% when compared. The inverted results closely match the measured values, accurately reflecting the dynamic changes in the physical properties of seabed surface sediments. Notably, the average grain size is a direct indicator of the sediment particles size with smaller particles found in deeper water. The variation characteristics of sediment physical parameters align well with the variation of sediment types in the canyon, which is consistent with changes in the water depth, topography, and hydrodynamic conditions of the area. This further demonstrates the reliability of the inversion results. [ABSTRACT FROM AUTHOR]

Based on the data of the acoustic and physical properties of seafloor sediments obtained on the continental shelf area of the East China Sea (ECS), prediction equations of the sediment sound speed based on single and dual parameters were established, and the correlation of the sound speed with physical parameters was discussed. The results show that the sediment sound speed (c) is strongly correlated with water content (w), density (ρ), void ratio (e), mean grain size (Mz) and median grain size (Md), and the coefficient of determination R2 of the empirical regression equation is generally greater than 0.80, while the empirical regression equation coefficient of determination R2 of the compression coefficient (a) and compression modulus (E) are slightly lower, with 0.79 and 0.73, respectively. The coefficients of determination of the dual parameter regression equations between sediment sound speed with physical property parameters are generally higher than those of the single parameter equations, which are all higher than 0.90 indicating better prediction performance. The sensitivity of the physical parameters to the sound speed was analyzed, and the result shows that the sequence of sensitivity from high to low influence on sediment sound speed is void ratio, density, compressibility coefficient, median grain size and mean grain size. The prediction equations established in this study are a good extension and supplement to marine geoacoustic models and is of great significance for obtaining the acoustic properties of the seafloor sediments on the shelf of the East China Sea. [ABSTRACT FROM AUTHOR]

The inversion method based on the reflection loss-grazing angle curve is an effective tool to obtain local underwater acoustic parameters. Because geoacoustic parameters vary in sensitivity to grazing angle, it is difficult to get accurate results in geoacoustic parameter inversion based on small-grazing-angle data in shallow water. In addition, the normal-mode model commonly used in geoacoustic parameter inversion fails to meet the needs of accurate local sound field simulation as the influence of the secant integral is ignored. To solve these problems, an acoustic data acquisition scheme was rationally designed based on a sparker source, a fixed vertical array, and ship drifting with the swell, which could balance the trade-off among signal transmission efficiency and signal stability, and the actual local acoustic data at low-to-mid frequencies were acquired at wide grazing angles in the South Yellow Sea area. Furthermore, the bottom reflection coefficients (bottom reflection losses) corresponding to different grazing angles were calculated based on the wavenumber integration method. The local seafloor sediment parameters were then estimated using the genetic algorithm and the bottom reflection loss curve with wide grazing angles, obtaining more accurate local acoustic information. The seafloor acoustic velocity inverted is c p = 1659   m / s and the sound attenuation is α p = 0.656   dB / λ in the South Yellow Sea. Relevant experimental results indicate that the method described in this study is feasible for local inversion of geoacoustic parameters for seafloor sediments. Compared with conventional large-scale inversion methods, in areas where there are significant changes in the seabed sediment level, this method can obtain more accurate local acoustic features within small-scale areas. [ABSTRACT FROM AUTHOR]

Based on the data of the sound speed and the physical–mechanical properties of 300 seafloor sediment samples collected in the middle area of the Southern Yellow Sea, a backpropagation (BP) neural network algorithm is used herein to study the neural network topology and the corresponding neuron characteristic factors. A neural network model for predicting the sound speed of the seafloor sediment based on density, water content, porosity, void ratio, liquid limit, plastic limit, and clay content in the middle area of the Southern Yellow Sea is established. The results show that the sound speed prediction accuracy is the highest when the network model contains seven input layer neurons, ten hidden layer neurons, and one output node. According to the prediction results, the MAE(Mean Absolute Error) and the MAPE(Mean Absolute Percentage Error) are 13.19 m s−1 and 0.85%, respectively. The prediction results illustrate that the established BP neural network prediction method for the seafloor sediment sound speed is better than the traditional single, two and multi-parameter prediction equations. [ABSTRACT FROM AUTHOR]

To measure and characterize the roughness spectrum of sea-bottom, which is an important input parameter of the bottom acoustic backscattering model, an underwater laser three-dimensional (3D) scanning system for measuring seafloor microtopography has been developed successfully. The proposed underwater laser 3D scanning system can provide 3D seafloor microtopography with the resolution of millimeter levels in both horizontal and vertical directions and obtain the roughness spectrum of sea-bottom further. Two sets of data, which were suitable to model low- and mid-frequency acoustic backscattering at 6–24 kHz, were collected in two sites of sandy bottom during the acoustic scattering experiment in the southern Yellow Sea. The one-dimensional (1D) and two-dimensional (2D) roughness spectrum of the seafloor have been calculated at the two sites named S1 and S2, respectively. The result shows that calculated 2D spectral indices and spectral intensities, respectively, were 3.45 and 2.17 × 10−4 m4 at S1 and 3.33 and 4.66 × 10−4 m4 at S2. 2 D roughness spectrum parameters were used to model the acoustic scattering at S1, and the result indicates that the measurement results of the seafloor roughness spectrum is suitable for modeling bottom acoustic scattering. [ABSTRACT FROM AUTHOR]

Based on the acoustic and physical data of typical seafloor sediment samples collected in the East China Sea, this study on the super parameter selection and contribution of the characteristic factors of the machine learning model for predicting the sound speed of seafloor sediments was conducted using the eXtreme gradient boosting (XGBoost) algorithm. An XGBoost model for predicting the sound speed of seafloor sediments was established based on five physical parameters: density (ρ), water content (w), void ratio (e), sand content (S), and average grain size (Mz). The results demonstrated that the model had the highest accuracy when n_estimator was 75 and max_depth was 5. The model training goodness of fit (R2) was as high as 0.92, and the mean absolute error and mean absolute percent error of the model prediction were 7.99 m/s and 0.51%, respectively. The results demonstrated that, in the study area, the XGBoost prediction method for the sound speed of seafloor sediments was superior to the traditional single- and two-parameter regressional equation prediction methods, with higher prediction accuracy, thus providing a new approach to predict the sound speed of seafloor sediments. [ABSTRACT FROM AUTHOR]

The acoustic characteristics of three fine-grained sediments (silty sand, silt, silty clay) in the South China Sea (SCS) were measured and analyzed at high frequency range of 27–247 kHz. The measurement results show that the sound speed dispersion is a positive linear relation at the measured frequency range, and the attenuation follows nonlinear frequency dependence, α = kfn, where n ranges from 0.59 to 0.85 for the three different sediments in the SCS. The frequency dependence of sound speed and attenuation were compared with the published literature. It was found that for silty clay, clayey silt, silt, and silty sand, the dispersion characteristics of these four sediments are basically consistent; in general, the dispersion of coarse particles is significant, and that of fine particles is weak, and permeability is the key parameter that determines the inflection point of high frequency to low frequency. By modeling these sediments with the Biot–Stoll model, it was found that the Biot–Stoll model can better predict the frequency-dependent characteristics of sound attenuation in a high-frequency band under the matching constraints of sound speed dispersion characteristics, indicating that the Biot–Stoll model has good applicability to different types of sediments in a high-frequency band. [ABSTRACT FROM AUTHOR]

The shear wave characteristics of shallow seafloor sediment are vital to marine acoustic study and ocean engineering exploration. We analyzed the shear wave speed and the physical properties of the southern Yellow Sea sediment based on the Grain-Shearing (GS) model. The results show that: (1) The shear wave speed of the high-porosity sediment is lower than that of the low-porosity sediment; (2) The shear wave speed of the fine-grained sediment is lower than that of the coarse-grained sediment; (3) The burial depth of the sediment has a significant influence on the shear wave speed of the seafloor sediment. The GS model agrees well with the measurement data from the southern Yellow Sea. However, the quantitative analysis of the impact of input parameters on the GS model requires further research. [ABSTRACT FROM AUTHOR]

The high-frequency acoustic properties of seafloor sediments are very significant in seafloor study and underwater acoustic study field. In order to measure the sound speed and the attenuation for the small-scale sediment cores more accurately, this study developed a water coupled acoustic laboratory measurement system based on Richardson-Briggs technique. This method used the correlation comparison of waveforms received in sediment core and in identical reference tubes filled with water to measure sound speed and attenuation. The sound speed and attenuation of a clayey silt sediment sample were measured using the water coupled acoustic laboratory measurement system. This frequency dependence of the sound speed and attenuation showed that the clayey silt sediment has a weak positive sound speed dispersion, while the attenuation increases with a strong positive gradient within the measurement frequency range. This study also noted that the measured sound speed ratio match well with the empirical equations from literature. The measured attenuation factor data can fall in the Hamilton's empirical prediction range. [ABSTRACT FROM AUTHOR]

In this study, shear wave speed and related physical properties were measured for 21 seafloor sediment cores collected in the northern South China Sea. Results reveal that the shear wave speed in this study area ranges between 15.57 and 75.55 m/s and demonstrates a pattern of regional distribution. The sediment in the continental shelf area shows characteristics of high shear wave speed, high wet bulk density, low porosity, and large grain size. Contrarily, the sediment in the continental slope area exhibits characteristics of low shear wave speed, low wet bulk density, high porosity, and fine particles. Accordingly, the correlation between shear wave speed and physical properties such as porosity, wet bulk density, water content, and average grain size was studied. Prediction formulas of shear wave speed of sediments in this study area containing single and double physical parameters were established via regression analysis. The comparison between single‐parameter and double‐parameter prediction formulas indicates that the determined coefficient of the double‐parameter prediction formulas based on porosity and average grain size is higher than that of single‐parameter prediction formulas. It indicates that the double‐parameter prediction formulas based on porosity and average grain size have better prediction performance. Moreover, the prediction formulas established in this study were further compared with those of other study areas, and their differences were analyzed accordingly. Plain Language Summary: The shear wave speed of seafloor sediments is the velocity of shear wave propagating in seafloor sediments. Shear wave speed is one of the critical acoustic properties of seabed sediments, which is very useful in many fields such as exploration and exploitation of seabed resources, precise prediction of the acoustic field, and prediction of stability with regard to engineering geology. In view of the importance of shear wave speed, the correlation between shear wave speed and physical properties such as porosity, wet bulk density, water content, and average grain size were analyzed, and the prediction formulas of shear wave speed were established in this study. Key Points: Shear wave speed and physical properties of sediments in the continental shelf were compared with those in the continental slopePrediction formulas of shear wave speed based on porosity, density, water content, average grain size were establishedThe prediction formulas established in this study were compared with those of other study areas, and their differences were analyzed [ABSTRACT FROM AUTHOR]

Three types of sediments were selected to measure their sound speed under changing temperature and pressure conditions in laboratory. The effects of temperature and pressure on sound speed in sediments and their trends were analyzed. The results showed that, with increasing temperature and pressure, the sound speed exhibits an increasing trend in all selected sediments. For each sample, the ratio of the sound speed in sediments to that in seawater almost remained unchanged at different pressures and temperatures, with a maximum fluctuation of 1.09% for temperature dependence and 0.68% pressure dependence. Combining the analysis of experimental results and sound speed correction procedure given by Hamilton, specific correction formulas of sound speed for temperature and pressure were presented. The laboratory-measured sound speed in the experiment and the sound speed obtained in the South Yellow Sea were corrected to reduce the effects of temperature and pressure using the correction formulas. The results show that the correction formulas with constant sound speed ratio are effective for correcting the sound speed measurement errors caused by changes in temperature and pressure. As a further consideration, the effects of the fluctuation of sound speed ratio on sound speed correction were analyzed. [ABSTRACT FROM AUTHOR]

A method of obtaining bottom backscattering strength by employing an omnidirectional projector and omnidirectional hydrophone is proposed. The backscattering strength is extracted from monostatic backscattering data. The method was adopted in an experiment conducted in the South Yellow Sea of China. The seafloor surface was relatively smooth and covered by a small quantity of shell fragments, as observed through a digital camera system. Sampling data showed that the main component of the sediment at this experimental site was fine sand. In this paper, we detail the calculation method. Preliminary results of backscattering strength as a function of grazing angle (20°–70°) in the frequency range of 6–24 kHz are presented. The measured backscattering strength increased with the grazing angle and changed more rapidly at large grazing angles (60°–70°). A comparison of the data at different frequencies reveals that the measured backscattering strength substantially rises with the increase of acoustic frequency. A fitting curve of Lambert's law against the measured data shows that the backscattering strength deviates from Lambert's law at large grazing angles. [ABSTRACT FROM AUTHOR]

Changes in seawater depth could cause variations of hydrostatic pressure and temperature conditions in surficial seafloor sediments. As a result, the sound speed of the sediments would change. Studies seldom have focused on the influence of hydrostatic pressure on such. According to calculations of the general model of sound speed (GMSS), the sound speed of sediment increased linearly when the hydrostatic pressure increased, the same trend as that of the seawater. Pressure-controlled experiments in the laboratory verified that the influence of hydrostatic pressure on the sediment sound speed was highly related to the pore water, which was consistent with the theoretical calculations from the GMSS. Hydrostatic pressure was found to have the same effect on sound speed for the three kinds of sediments: gravel-silty sand, clayey silt, and silty clay. For different hydrostatic pressures up to 40 MPa, the fluctuation of sound speed ratio calculated with the theoretical model was less than 1.96%, while that measured in the controlled experiment was less than 2.63%. This means that if the sound speed ratio is used as a constant to correct for the difference at different seawater depths of no more than 4000 m, the estimable error is about 2.63%. [ABSTRACT FROM AUTHOR]

To accurately characterize sound speed dispersion of shallow sediments in the Southern Yellow Sea, three types of sediments, i.e., silt, clayey silt, and silty clay, were selected to measure the sound speeds at 25-250 kHz. Over the frequency range, the sound speeds vary approximately from 1,536 to 1,565 m s−1 in silt sediment, from 1,511 to 1,527 m s−1 in clayey silt sediment, and from 1,456 to 1,466 m s−1 in silty clay sediment. The sound speed exhibits a slow increase with frequency in a nearly linear gradient, but these three types of sediments have different sound speed dispersion characteristics. The silt sediment with relatively coarse grains has the most significant sound speed dispersion, while the sound speed dispersions of the two others are relatively weak. Comparison between the measured dispersions and the model predictions shows that the grain-shearing model can match the measured data at most of frequencies. Nevertheless, when the grain bulk modulus was assigned 3.2 × 1010 Pa according to relevant references, the Biot-Stoll model predictions were higher than the measured values at high frequencies; when it was assigned a relatively small value of 2.8 × 1010 Pa, the model predictions achieved optimal matching with the measured values. [ABSTRACT FROM AUTHOR]

Acoustic bottom scattering is the main source of reverberation background in target detection and recognition near the seafloor. Accurate estimation of bottom scattering strength is necessary for prediction of sonar performance in the presence of reverberation in shallow-water environments. A measurement of bottom backscattering strength at midfrequency in a shallow-water environment of the South Yellow Sea of China was carried out in April 2016, where the water depth was 39 m. The surface of the seafloor was relatively flat and covered by some shell fragments observed through a digital camera system. Core data showed that the main component of sediment at this experiment site was fine sand. The backscattering strength as a function of grazing angle (20°–70°) in the frequency range of 6–12 kHz was extracted from monostatic backscattering data, employing an omni-directional source and an omni-directional hydrophone. The measured backscattering strength increases with the increase of the grazing angle, and changes more rapidly at large grazing angles (60°–70°). Comparing the data at different frequencies, it is found that the measured backscattering strength slightly increases with the increase of the acoustic frequency. A fit of Lambert's law to the measured data shows that the backscattering strength deviates from the Lambert's law at large grazing angles. Finally, the fitted result of a scattering model to the measured data suggests that seafloor roughness scattering is larger than that from sediment volume heterogeneity in the measurement frequency band. [ABSTRACT FROM AUTHOR]

The mechanical structure, the function modules, the working principles, and a sea trial of the newly developed ballast in situ sediment acoustic measurement system are reported in this study. The system relies on its own weight to insert transducers into seafloor sediments and can accurately measure the penetration depth using a specially designed mechanism. The system comprises of an underwater position monitoring and working status judgment module and has two operation modes: self-contained measurement and real-time visualization. The designed maximum working water depth of the system is 3,000 m, and the maximum measured depth of seafloor sediment is 0.8 m. The system has one transmitting transducer with the transmitting frequency band of 20-35 kHz and three receiving transducers. The in situ acoustic measurement system was tested at 15 stations in the northern South China Sea, and repeated measurements in seawater demonstrated good working performance. Comparison with predictions from empirical equations indicated that the measured speed of sound and attenuation fell within the predicted range and that the in situ measured data were reliable. [ABSTRACT FROM AUTHOR]

AbstractBased on the sound speed and physical property measurements of seafloor sediments obtained in the central part of the South Yellow Sea, a multi-parameter prediction model for the sound speed of seafloor sediments based on seven parameters, including density, porosity, water content, liquid limit, plasticity index, compression coefficient, and median grain size, was developed by using the BP-AdaBoost fusion algorithm. The results show that the model, configured with 7 input layer neurons, 10 hidden layer neurons, 1 output node, a learning rate of 0.1, and 300 training iterations, achieved a correlation coefficient of 0.962. The mean absolute error (MAE) of the predicted sound speed was 10.19 m/s, and the mean relative error (MAPE) was 0.66%. These results are better than those of single-parameter and dual-parameter prediction equations and other machine learning models. The BP-AdaBoost model prediction method of sound speed of seafloor sediments established in this paper is better than the single-parameter and dual-parameter prediction equations and other prediction models, and it can provide a new way for the prediction of sound speed of seafloor sediments in the study area. [ABSTRACT FROM AUTHOR]