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.

This article presents a method for using a conventional high-voltage cable as a transducer and exciting it with a pulse power supply to generate perfect cylindrical acoustic waves in water. The experimental results prove that the acoustic pulse strictly follows the propagation law of cylindrical waves. There is a slight fluctuation of sound pressure in the direction parallel to the cable and no directivity in the plane perpendicular to the cable. Under excitation energy of 1000 J, the duration of the acoustic pulse generated by a 12-m cable is about 500 μ s, the dominant frequency is around 3000 Hz, the effective source level reaches 195 dB, and the energy conversion efficiency is 0.012%. This method breaks through the traditional mode of forming a line source by combining point sources. The vibration and acoustic radiation along the cable are continuous, and the cylindrical waves are very stable. Based on the comparative analysis of marine single-channel seismic exploration, line source shows the potential to change the traditional method based on point source and spherical wave theory. It provides a new method for simplifying the survey system and improving exploration accuracy.

This paper proposes a novel deep learning-based waveform inversion method that enables rapid and high-precision extraction of nonlinear waveform information from seismic data. To address critical challenges in existing deep learning full waveform inversion (FWI), such as poor network generalization and huge computational cost, we introduce angle-domain generalized Radon transform (AD-GRT). As intermediate data, AD-GRT generated angle-domain gathers are equivalent to the original seismic data. Instead of seismic data, the angle-domain gathers serve as input for the new waveform inversion deep network. To ensure the waveform nonlinearity of the angle-domain gathers is local, this method requires a basically accurate background velocity model to guarantee that the contour lines in angle-domain gathers have no significant curvature, and avoids inversion in areas covered by strong reflectors like salt. Relying on the original data equivalence and the local waveform nonlinearity of the angle-domain gathers, we developed a horizontally layered model-based CNN network for local nonlinear waveform mapping between angle-domain gathers and true models, achieving a highly accurate and strongly generalized waveform inversion network with minimal training data. The generalization and accuracy of the CNN network are validated using random layered models. Furthermore, the CNN network is directly extended to 2D media waveform inversion. Three complex 2D models confirm the high accuracy, stability and robustness of the CNN network. Additionally, the computational cost of the CNN network waveform inversion is negligible, compared with a single finite-difference simulation.

This study addresses the applicability of conventional marine acoustic technologies for detecting non-metal artifacts. Based on the typical environment in Xiamen Bay, we evaluated the detection efficacy of common multibeam sonar, side-scan sonar, and sub-bottom profiling sonar through a controlled model experiment system. We employed ceramic artifact replicas (ranging in size from 10 to 70 cm) and incorporated acoustic parameter optimization to elucidate the applicability boundaries of different technologies. The results indicate that multibeam sonar can identify clustered targets larger than 0.5 m, but is limited in resolving small individual targets (less than 30 cm) due to terrain detail constraints. Side-scan sonar, under low-speed (less than 4 knots) and near-bottom operating conditions, effectively captures the high-intensity echo characteristics of ceramic targets, achieving a maximum effective detection range of more than 40 m. High-frequency sub-bottom profiler (94–110 kHz) offers resolution advantages for exposed artifacts, while low-frequency signals (5–15 kHz) provide theoretical support for detecting subsequently buried targets. Furthermore, the study quantifies the coupling effects of substrate type, target size, and surface roughness on acoustic responses. We propose a synergistic detection workflow comprising “multibeam initial screening—side-scan fine mapping—sub-bottom profiling validation,” which provides empirical support for the optimization and standardization of underwater archeological technologies in complex marine environments.