Editorial: Broadband Seafloor Sediment Acoustic Property and multi-parameter Geoacoustic Model

Guangming Kan ^1,2 Baohua Liu ³ Xingsen Guo ^4,5 Jingqiang Wang ^1,2*

• ¹ First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China
• ² Key Laboratory of Submarine Acoustic Investigation and Application of Qingdao(Preparatory), Qingdao, China
• ³ Laboratory for Marine Geology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong Province, China
• ⁴ Department of Civil, Environmental & Geomatic Engineering, Faculty of Engineering Sciences, University College London, London, England, United Kingdom
• ⁵ Shandong Province Key Laboratory of Marine Environment and Geological Engineering, Ocean University of China, Qingdao, Shandong Province, China

The seafloor is an important boundary of the ocean sound field, and the acoustic property of seafloor sediment and its spatial distribution are important factors that affect the propagation and variation of sound waves in the ocean. The research of seafloor sediment acoustic property (geoacoustic property) is an interdisciplinary subject involving marine geology, marine geophysics, and marine acoustics. The research of geoacoustic property mainly includes measurement techniques on geoacoustic property, the impacting factors on the geoacoustic property, the relationship between geoacoustic property and the physical-mechanical parameters (geoacoustic model), application of geoacoustic property, and so on. The research of geoacoustics has important practical value and significance in many Measurement technologies for acoustic and physical properties of seabed sediments Jiao et al. (2024) conducted cyclic shear tests of the natural marine clay of the South China Sea, with varying the cyclic stress ratio (CSR), overpressure consolidation ratio (OCR), consolidation ratio (Kc), and loading frequency. They found that the CSR, OCR, and Kc significantly impact the cumulative dynamic strain in deep-sea soft clay during undrained cyclic dynamic tests. Higher CSR values lead to increased dynamic strain and structural failure risk. They also proposed a dynamic strain-dynamic pore pressure development model, which can effectively capture the cumulative plastic deformation and dynamic pore pressure development, showing correlations with the CSR, OCR, and Kc, thus providing insights into the deformation and pore pressure trends in deep-sea clay under high cyclic dynamic loading conditions. This study not only furnishes essential background information but also addresses a critical gap in understanding the behavior of deep-sea soft clay under cyclic loading, thereby enhancing the safety and stability of seabed structures. Li et al. (2023) presented an array geometry inversion method suitable for complex seafloors to address the challenge of precise source-receiver positioning with deep-towed multichannel seismic systems. An objective function of the deep-towed seismic array geometry inversion was built using the shortest path algorithm according to the travel times of direct waves and seafloor reflections, and the high-precision inversion of the source-receiver position was achieved by using the particle swarm optimization (PSO) algorithm. The results verified the effectiveness of the method proposed in this paper, especially its applicability in scenarios with dramatic changes in seabed topography. This study provides insights into the accuracy and reliability of the proposed geometric shape inversion method for deep-towed seismic arrays in practical applications to meet the requirements of near-bottom acoustic detection for fine imaging of deep-sea seabed strata and precise inversion of geoacoustic parameters. Sampling System, which can be used to perform multi-parameter in situ testing and low-disturbance sampling of 3000 m deep-sea seabed sediments. The system adopts electrohydraulic proportional position control and a fuzzy PID controller to precisely control the position of the piston of the hydraulic circuit, which can improve the accuracy of the cone test data and reduce the interference of the sampling tube with the original sediment during the sampling process. Moreover, electrohydraulic co-simulation of the hydraulic control system was conducted with AMESim and Simulink software, and the position control and speed control effects of the system were verified. The system was tested on site in the Shenhu Sea area of the South China Sea, and obtained 9 in-situ parameters, including physical and chemical parameters, for sediments within a depth range of 2.66 m on the seabed surface at a depth of 1820 m. The testing results of the system accurately and efficiently reflect the property characteristics of seafloor sediments in an in situ environment, indicating the system can be widely used in marine engineering geological investigations and measurement of physical parameters of seafloor sediment. Zhen et al. (2024) developed an acoustic reflection measurement system using a self-developed, high-precision, high-frequency shallow stratigraphic profiler to perform the sediment grain size classification. In this study, they utilized this system to analyze six sandy sediments with different grain sizes in the laboratory. The result shows a positive correlation between the amplitude of the acoustic reflection echo and grain size, and the amplitude of the reflection peaks increased with increasing grain size. By analyzing the amplitude of the reflection peaks and echo waveform, sediment grain sizes can be distinguished in a more precise manner. This study provides a valuable guide for the fine-grained classification of sediment grain size. Lee et al. (2024) estimate the geoacoustic parameter values at low frequency for the two-layer geoacoustic bottom model by comparing the dispersion curves extracted from the replicas predicted by the KRAKEN normal-mode program with dispersion curves extracted from airgun sounds received in the East Siberian Sea. The result revealed the best-fit values for the sediment sound speed and density in the surficial layer to be approximately 1422.4 m/s and 1.58 g/cm 3 , respectively. For the lower layer, these values were estimated to be 1733.6 m/s and 1.84 g/cm 3 , respectively, and the surficial sediment thickness was estimated to be ~ 4.1 m. Subsequently, the distances between the airgun and the receiver system in the 18.6 to 121.5 km range were calculated by comparing the measured modal curves and the model replicas predicted using the estimated geoacoustic parameters. In order to mitigate the distance errors, they employed an adiabatic approximation for model propagation in the range-dependent environment. The modeled modal travel times were calculated by dividing the source-receiver distance into range-independent segments, each based on a 1-m change in water depth, and then summed. The result shows that the re-estimated distance error is reduced to within 10%, indicating the method of geoacoustic inversion presented in this study is effective. Meng et al. (2024) established a machine learning model for predicting the shear wave speed of seafloor sediments in the northwest South China Sea, using the eXtreme Gradient Boosting (XGBoost) algorithm. By optimizing the hyperparameters of the model, the best fit of the XGBoost algorithm is obtained when the n_estimator and max_depth are 115 and 6, respectively. The mean absolute error and the goodness of fit between the predicted values and validation data are 3.366 m/s and 9.90%, respectively. They compared the multi-parameter shear wave speed prediction model established in this study with the single-parameter prediction models, the dual-parameter prediction models, and the GS prediction model, and the result indicates that the multi-parameter shear wave speed prediction model based on the XGBoost algorithm has the lowest MAE and MAPE between the test data and the predicted values, which are 4.04 m/s and 14.3%, respectively. This study indicates that the multi-parameter shear wave speed prediction model based on the XGBoost algorithm has a higher accuracy for predicting the shear wave speed in the northwest South China Sea. analyze the variation of acoustic properties with the porosity of seafloor sediments. They employed P-EDFM to investigate the influence of physical parameters, including porosity and density, as well as temperature environment, and measurement frequency on the in situ sound velocity and sound attenuation coefficient of seafloor sediments. According to the P-EDFM, the in situ sound velocity ratio decreases with increasing bulk porosity and with decreasing bulk density. After considering the influence of temperature in the P-EDFM, the prediction of in situ sound velocity aligns well with the measured dataset. The acoustic attenuation coefficient exhibits an inflection point, increasing initially and then decreasing with changes in porosity, similar to the observed pattern in Hamilton's observation and estimation. Overall, P-EDFM can predict the in situ sound velocity and sound attenuation coefficient under different temperatures and frequencies, with a lower prediction error for sound velocity compared to the sound attenuation coefficient.