JOURNAL ARTICLE
Dispersion and attenuation of guided waves in the Arctic Ocean with viscoelastic ice layer.
Published In: Journal of the Acoustical Society of America, 2025, v. 157, n. 5. P. 3296 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Zeng, Qitian; Liu, Shengxing; Tang, Liguo; Li, Zhenglin; Luo, Wenyu 3 of 3
Abstract
This article focuses on applying the spectral method to analyze the complex dispersion and attenuation characteristics of guided acoustic waves in a viscoelastic ice–water–sediment–seafloor coupled system modeling the Arctic Ocean environment. The study formulates the wave equations and boundary conditions into a generalized eigenvalue problem using Chebyshev and Laguerre orthogonal polynomials for discretization, enabling efficient and accurate numerical computation of dispersion and attenuation curves. Numerical results demonstrate that ice layer damping properties have a more significant impact on guided-wave attenuation than ice thickness, with the first-order mode exhibiting notably higher attenuation that increases monotonically with frequency, while higher-order modes show frequency-selective attenuation patterns influenced by seawater depth. The spectral method is shown to outperform traditional numerical techniques in efficiency, accuracy, and coding simplicity, offering valuable theoretical guidance for Arctic acoustic detection, navigation, and communication despite the model’s simplifications relative to the actual Arctic environment.
Additional Information
- Source:Journal of the Acoustical Society of America. 2025/05, Vol. 157, Issue 5, p3296
- Document Type:Article
- Subject Area:Environmental Sciences
- Publication Date:2025
- ISSN:0001-4966
- DOI:10.1121/10.0036568
- Accession Number:185593235
- Copyright Statement:Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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