Towards addressing the issues of poor image quality and limited sample quantity in underwater sonar images. A genetic encoding method for the fusion of image spot and local binary pattern texture features is employed in this study. By encoding the fusion features using genetic algorithms, the dimensionality of the fused features is reduced, thereby enhancing the efficiency of neural network operations while ensuring data transmission confidentiality. Additionally, a deep multi-classifier is introduced, which combines residual units with CNN-BiGRU-Attention. This classifier takes the genetically encoded fused features as input and outputs four predicted probabilities. Through an ensemble learning strategy that evenly assigns weights to the predicted probabilities, the final prediction result is obtained, achieving underwater target recognition. Results demonstrate that the proposed fusion features effectively enlarge inter-class differences among various types of sonar images and improve classifier accuracy. The deep multi-classifier model exhibits stable improvement in prediction accuracy compared to the original model, converging after only 15 training iterations with an average recognition accuracy of 99.1%. The research findings offer an effective approach for target sonar image recognition and classification, with potential applicability to a wider range of underwater target categorization.

The coupling mechanism between natural and anthropogenic noise in shallow marine areas is of great significance for maintaining the ecological safety of these regions. In this study, a section of Zhanjiang Bay’s entrance was selected as a typical research area, and environmental noise data at different depths were collected during the spring and autumn seasons. The spectral characteristics, sound pressure levels, and underwater noise frequency correlation matrices of environmental noise were analyzed to reveal the underwater noise characteristics of tidal channels in Zhanjiang Bay and their main influencing factors. The results show that underwater noise in this study area had a stable frequency band distribution. In the low-frequency range of 20–50 Hz, the main source of noise was the flow noise influenced by tides and topography, with a peak sound pressure level of approximately 97 dB. In the frequency range of 50 Hz to 500 Hz, the main noise sources were ships at sea, followed by wind-generated noise. At frequencies above 500 Hz, the noise intensity decreased. In addition, it was found that the sound pressure level in the low-frequency range had a significant correlation with the tidal level, increasing with the rise of the tide and decreasing during low tides. This study provides a research case on the impact that human noise activity has on environmental noise in shallow marine bays. These research findings can support the selection of sites and reduce construction noise from offshore wind farms, as well as ensure the acoustic ecological environment in the vicinity of marine ranches.

Single-channel seismic (SCS) methods play a crucial role in offshore wind farm assessments, offering rapid and continuous imaging of the subsurface. Conventional SCS methods often fall short in resolution and signal completeness, leading to potential misinterpretations of geological structures. In this study, we propose the application of seismic interferometry as a powerful tool to address these challenges by utilizing multiple reflections that are usually considered as noise. First, we demonstrate the feasibility of using seismic interferometry to approximate the primary wavefield. Then, we evaluate a series of seismic interferometry applied in SCS data, including cross-correlation, deconvolution, and cross-coherence, and determine the most appropriate one for our purpose. Finally, by comparing and analyzing the differences in amplitude, continuity, time–frequency properties, etc., between conventional primary wavefield information and reconstructed primary wavefield information by seismic interferometry, it is proved that incorporating multiples as supplementary information through seismic interferometry significantly enhances data reliability and resolution. The introduction of seismic interferometry provides a more detailed and accurate geological assessment crucial for optimal site selection in offshore wind farm development.

The construction of wind farm pilings, submarine pipelines, and underwater submarines involves multiple cylinders. However, there is currently a lack of economic research on predicting the mechanism and characteristics of mutual coupling of acoustic scattering from multiple cylindrical targets. This study investigates the mechanism and prediction method of acoustic scattering for the structural distribution characteristics of underwater multi-cylindrical targets. A model of a multi-cylindrical target’s two-dimensional acoustic field was established using the finite element method. Numerical calculations were then carried out to elucidate the scattering characteristics of the frequency–angle spectrum in far-field omnidirectional scattering. The simulation of echoes in the time domain explains how echoes propagate and interact with each other, and provides formulas for calculating interference and resonance frequencies. The frequency calculation formula extracts key features from the spectrum, providing a basis for predicting the characteristics of multi-cylindrical targets in terms of scale and spatial position. Measurement experiments were conducted on a double-cylindrical target in a water tank, and the theoretical calculations and experimental data were used to estimate the target’s radius and distance. The actual layout confirms the accuracy of the interference and resonance frequency prediction formulas. This study offers a valuable solution for refined feature extraction and spatial estimation of underwater targets.

The acoustic scattering of targets in shallow-sea waveguides exhibits complex features such as multipath propagation and intricate echo components, with its acoustic field properties remaining incompletely understood. This study employs a hybrid method combining normal modes and scattering functions to numerically model the acoustic scattering of targets in waveguide channels. We analyze the coupling mechanisms of multipath acoustic waves and derive precise predictive formulas for the bright–dark interference fringe patterns in range–frequency spectra based on the physical mechanisms governing acoustic field interference. By tracking the peak trajectories of these interference fringes in range–frequency spectra, we investigate the variations of the waveguide invariant with frequency, range, and depth, revealing statistical patterns of the waveguide invariant in target–waveguide coupled scattering fields under different water depths. The results demonstrate that, under constant channel conditions, waveguide properties exhibit a weak correlation with target material characteristics. In shallow water environments, waveguide invariant values display broader distributions with higher probability density peaks, whereas increasing water depth progressively narrows the distribution range and monotonically reduces the peak magnitudes of the probability density function. Experimental validation via scaled elastic target echo testing confirms the observed trends of waveguide invariant variation with water depth.

Assessment of the effective state concentrations of Zn, Pb, Cu, Ni, Cr, Cd, and As in 210 soil samples across the Guangzhou region in South China. The study also explores the pollution status, potential risks, and identifies the main hazard factors associated with these elements. The analysis of effective concentrations indicates average values of 13.66 mg/kg for Pb, 3.64 mg/kg for Ni, 17.92 mg/kg for Cu, 1.74 mg/kg for Cr, 51.77 mg/kg for Zn, 0.22 mg/kg for Cd, and 0.38 mg/kg for As. Notably, all heavy metals exhibited considerable variability. The single pollution index method and Nemerow index method both suggest that, with the exception of Cd, all heavy metals exhibit relatively low levels of contamination, indicating a slight overall pollution in the studied samples. The potential ecological risk index identifies Cd as the primary ecological risk factor among the available heavy metals in the soil of Guangzhou. The analysis using the potential health risk model suggests that the available forms of each heavy metal do not pose substantial health risks to both children and adults. The principal non-carcinogenic factors identified are Cu and Pb, whereas Cd and Cr emerge as the primary carcinogenic factors. The collaborative risk assessment analysis revealed that 79% of the samples in Guangzhou exhibited low potential risks, 20% showed potential risks, and only one sample indicated serious risks.