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Predicting underwater acoustic transmission loss in the SOFAR channel from ray trajectories via deep learning.
- Source :
- JASA Express Letters; May2024, Vol. 4 Issue 5, p1-8, 8p
- Publication Year :
- 2024
-
Abstract
- Predicting acoustic transmission loss in the SOFAR channel faces challenges, such as excessively complex algorithms and computationally intensive calculations in classical methods. To address these challenges, a deep learning-based underwater acoustic transmission loss prediction method is proposed. By properly training a U-net-type convolutional neural network, the method can provide an accurate mapping between ray trajectories and the transmission loss over the problem domain. Verifications are performed in a SOFAR channel with Munk's sound speed profile. The results suggest that the method has potential to be used as a fast predicting model without sacrificing accuracy. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 26911191
- Volume :
- 4
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- JASA Express Letters
- Publication Type :
- Academic Journal
- Accession number :
- 179537027
- Full Text :
- https://doi.org/10.1121/10.0025976