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.

The acoustic properties of seafloor sediments have always been important parameters in sound field analyses and exploration for marine resources, and the accurate acquisition of the acoustic properties of sediments is one of the difficulties in the study of underwater acoustics. In this study, sediment cores were taken from the northern South China Sea, and the acoustic properties were analyzed. Since traditional methods (such as regression equations or theoretical models) are difficult to apply in practical engineering applications, we applied remote sensing data to sound velocity prediction models for the first time. Based on the influencing mechanism of the acoustic properties of seafloor sediments, the sediments’ source, type and physical properties have a great influence on the acoustic properties. Therefore, we replaced these influencing factors with easily accessible data and remote sensing data, such as parameters of granularity, distance to the nearest coast, decadal average sea surface productivity, water depth, etc., using deep neural networks (DNN) to develop a sound velocity prediction model. Compared with traditional mathematical analyses, the DNN model improved the accuracy of prediction and can be applied to practical engineering applications.

The Water and Sediment Regulation Scheme (WSRS), implemented since 2002, has been essential for controlling water flow and mitigating sediment siltation in the lower Yellow River. However, WSRS was suspended for the first time in 2016 and 2017 due to extremely low water flow. The rapid floodwater discharge over roughly 20 days conducted by WSRS strongly impacts total suspended solids (TSS) distribution in the Yellow River Estuary (YRE). This study employs high-frequency Sentinel-3 OLCI satellite imagery to investigate intraday TSS variations in the YRE under new water-sediment regulation conditions from 2016 to 2023. TSS concentrations were generally low during the 2016 and 2017 flood seasons, but increased markedly after WSRS resumed in 2018. Peak TSS values occurred in July or August, sometimes extending into September and October during autumn floods. A moderately strong positive correlation was observed between TSS concentrations at the river mouth and sediment load at Lijin Station during the flood seasons. The 2018 WSRS event generated an extensive river plume, with average TSS concentrations at the river mouth exceeding 400 g·m⁻³. From 2018 to 2023, TSS concentrations exhibited a declining trend during flood seasons, attributed to reduced sediment discharge and ongoing sediment accretion in the Yellow River Delta. Our findings highlight Sentinel-3 OLCI as a powerful tool to resolve WSRS-driven sediment dynamics, offering critical guidance for estuarine management.