The bowhead whale is a vital component of the maritime environment. Using deep learning techniques to recognize bowhead whales accurately and efficiently is crucial for their protection. Marine acoustic remote sensing technology is currently an important method to recognize bowhead whales. Adaptive SWT is used to extract the acoustic features of bowhead whales. The CNN-LSTM deep learning model was constructed to recognize bowhead whale voices. Compared to STFT, the adaptive SWT used in this study raises the SCR for the stationary and nonstationary bowhead whale whistles by 88.20% and 92.05%, respectively. Ten-fold cross-validation yields an average recognition accuracy of 92.85%. The method efficiency of this work was further confirmed by the consistency found in the Beaufort Sea recognition results and the fisheries ecological study. The research results in this paper help promote the application of marine acoustic remote sensing technology and the conservation of bowhead whales.

Mesoscale eddies have an impact on the marine environment mainly in two areas, namely, currents and changes in the sound velocity gradient due to temperature and salt stirring. The traditional underwater-related remote sensing acoustic remote sensing model is capable of analyzing the acoustic field under the change in sound velocity gradient, but it is not capable of analyzing the acoustic field under the influence of ocean currents. In order to more effectively analyze the changes in the acoustic field caused by mesoscale eddies, this paper proposes a FEM flow impact model applied to the rapid computation of acoustic remote sensing of mesoscale eddies in the sea area. The algorithm first performs a grid optimization of the sea area model based on vertical sound velocity variations and completes the classification of sound velocity layer junctions. At the same time, we construct the sound velocity gradient environment affected by the mesoscale eddy and then simplify the fluid flow in the mesoscale eddy into a non-viscous and non-rotating velocity potential, and then participate in the solution of the three-dimensional spatial fluctuation equations in the form of time-harmonic in the frequency domain, from which we can obtain the truncated sound pressure as well as the propagation loss, and quickly and completely characterize the acoustic remote sensing of the sea area of the mesoscale eddy. The paper verifies the effectiveness of the algorithm through SW06-contained flow experiments and further proposes an optimization formula for sound velocity inversion. We analyze this using measured data of mesoscale eddy fields in the Bering Sea waters during 2022 and find that eddies have a greater effect on the propagation of the acoustic field along their flow direction.

With the advancement of scientific research, understanding the physical parameters governing acoustic wave propagation in sea ice has become increasingly important. Among these parameters, shear wave velocity plays a crucial role. However, as measurements progressed, it became apparent that there was a large discrepancy between measured values of shear waves and predictions based on empirical formulas or existing models. These inconsistencies stem primarily from the complex internal structure of natural sea ice, which significantly influences its physical behavior. Research reveals that shear wave velocity is not only influenced by bulk properties such as density, temperature, and stress state but is also sensitive to microstructural features, including air bubbles, inclusions, and ice crystal orientation. Compared to longitudinal wave velocity, the characterization of shear wave velocity is far more challenging due to these inherent complexities, underscoring the need for more precise measurement and modeling techniques. To address the challenges posed by the complex internal structure of natural sea ice and improve prediction accuracy, this study introduces a novel, integrated approach combining simulation, measurement, and inversion intelligent learning model. First, a laboratory-based method for generating sea ice layers under controlled formation conditions is developed. The produced sea ice layers align closely with measured values for Poisson’s ratio, multi-year sea ice density, and uniaxial compression modulus, particularly in the high-temperature range. Second, enhancements to shear wave velocity measurement equipment have been implemented. The improved device achieves measurement accuracy exceeding 1%, offers portability, and meets the demands of high-precision experiments conducted in harsh polar environments. Finally, according to the characteristics of small sample data. The ANN neural network was improved to a deep residual neural network with the addition of Squeeze-and-Excitation Attention (SE-ResNet) to predict longitudinal and transverse wave velocities. This prediction method improves the accuracy of shear and longitudinal wave velocity prediction by 24.87% and 39.59%, respectively, compared to the ANN neural network.