Xianfeng Li

Despite their importance for constraining lithospheric magnetization models, tracking secular variation in oceanic regions, and improving global geomagnetic field representations, long-term vector geomagnetic data from deep-sea environments remain scarce. In this study, we developed a three-axis fluxgate magnetometer mounted on a deep-sea mooring platform to acquire continuous vector data at 580 m depth in the Western Pacific Ocean and assessed its performance through a 48 h observatory comparison and a 20-day at-sea trial. During the observatory test, the magnetometer achieved an instrumental accuracy of 0.2 nT (characterized under stable observatory conditions) and a noise floor of 0.09 nT, with Pearson correlations of 93.76–94.13% against reference scalar magnetometers. In the sea trial, the attitude-corrected, vector-synthesized total field agreed with two co-deployed Sentinel magnetometers at 230 m and 380 m depths, yielding Pearson correlations of 94.33% and 94.59%, respectively. All instruments coherently recorded diurnal variations with a peak-to-peak amplitude of approximately 50 nT. The inter-depth differences in mean total field—35,353 nT at 230 m, 35,337 nT at 380 m, and 35,285 nT at 580 m—are primarily attributable to uncalibrated instrument baselines, with secondary contributions from residual attitude correction errors. These results demonstrate that mooring platforms can support multi-depth vector geomagnetic observations over deployment timescales of weeks to months, providing a pathway toward spatially distributed deep-sea geomagnetic field monitoring.

This study investigates the prediction of the acoustic impedance of submarine sediments in the middle area of the South Yellow Sea using the Random Forest (RF) model algorithm. A predictive model for the acoustic impedance of submarine sediments was established using a Random Forest algorithm based on six characteristic factors, including density, porosity, liquid limit, moisture content, plasticity index, and median particle size. The results indicate that the highest prediction accuracy and lowest error were achieved when n_estimator was set to 27, max_depth to 8, and min_samples_leaf to 7. The model significantly outperformed traditional single-parameter regression equations. The coefficient of determination (R²) of the test set reached 0.991 after model training, the mean absolute error (MAE) was 23.14 × 10³ kg/(m²·s), and the mean absolute percentage error (MAPE) was 0.90%. This paper provides an in-depth analysis of the relationship between acoustic impedance and various physical and mechanical properties, providing valuable guidance for advancing the prediction of acoustic impedance of submarine sediments.

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.