It is one of the most concerning problems in hydroacoustics to find a method that can calculate the acoustic propagation accurately and adapt to the variation of the seabed. Currently, the one-dimensional spectral method has been employed to address the simplified ocean acoustic propagation model successfully. However, due to the model's application limitations and approximation error, it poses challenges when attempting to solve real-world ocean acoustic fields. Hence, there is a crucial need to develop a direct solution method for the two-dimensional Helmholtz equation of ocean acoustic propagation, without relying on a simplified model. In previous work, we achieved successful solutions for the two-dimensional Helmholtz equation within a rectangular domain, utilizing a collocation-type spectral method. Taking into account the fluctuations in the actual seabed, we introduce a Chebyshev collocation spectral method to directly tackle the two-dimensional ocean acoustic propagation problem, which could solve the case of a seabed with linear variation, sound velocity variation and inhomogeneous medium situation. After comparative verification, the calculation result of the two-dimensional spectral method is more accurate than traditional mature models such as Kraken and COUPLE. By eliminating model constraints and enlarging the solution range, this spectral method holds immense potential in real marine environments.
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http://dx.doi.org/10.1121/10.0034411 | DOI Listing |
Sensors (Basel)
January 2025
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
Underwater acoustic transducers need to expand the coverage of acoustic signals as much as possible in most ocean explorations, and the directivity indicators of transducers are difficult to change after the device is packaged, which makes the emergence angle of the underwater acoustic transducer limited in special operating environments, such as polar regions, submarine volcanoes, and cold springs. Taking advantage of the refractive characteristics of sound waves propagating in different media, the directivity indicators can be controlled by installing an acoustic lens outside the underwater acoustic transducer. To increase the detection range of an underwater acoustic transducer in a specific marine environment, a curvature-determining method for the diverging acoustic lens of an underwater acoustic transducer is proposed based on the acoustic ray tracing theory.
View Article and Find Full Text PDFMar Pollut Bull
January 2025
Department of Biology, University of Victoria, Victoria, BC V8P 5C2, Canada.
Anthropogenic noise pollution has been accelerating at an alarming rate, greatly altering aquatic soundscapes. Animals use various mechanisms to avoid acoustic masking in noisy environments, including altering calling rates or the frequency (pitch) of their vocalizations or increasing the amplitude (loudness) of their vocalizations (i.e.
View Article and Find Full Text PDFScience
January 2025
Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, nipaluna/Hobart, Tasmania, Australia.
Vertical migrations by marine organisms contribute to carbon export by consumption of surface phytoplankton followed by defecation in the deep ocean. However, biogeochemical models lack observational data, leading to oversimplified representation of carbon cycling by migrating organisms, such as Antarctic krill (). Using a numerical model informed by 1 year of acoustic observations in the East Antarctic, we estimated the total particulate organic carbon (POC) flux from krill fecal pellets to be 9.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, People's Republic of China.
A complex-valued neural process method, combined with modal depth functions (MDFs) of the ocean waveguide, is proposed to reconstruct the acoustic field. Neural networks are used to describe complex Gaussian processes, modeling the distribution of the acoustic field at different depths. The network parameters are optimized through a meta-learning strategy, preventing overfitting under small sample conditions (sample size equals the number of array elements) and mitigating the slow reconstruction speed of Gaussian processes (GPs), while denoising and interpolating sparsely distributed acoustic field data, generating dense field data for virtual receiver arrays.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China.
Underwater acoustic propagation is a complex phenomenon in the ocean environment. Traditional methods for calculating acoustic propagation loss rely on solving complex partial differential equations. Deep learning methods, leveraging their robust nonlinear approximation capabilities, can model various physical phenomena effectively, significantly reducing computation time and cost.
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