Efficient Prediction of Shallow-Water Acoustic Transmission Loss Using a Hybrid Variational Autoencoder–Flow Framework

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書誌詳細
出版年:	Journal of Marine Science and Engineering vol. 13, no. 7 (2025), p. 1325-1348
第一著者:	Bolin, Su
その他の著者:	Wang, Haozhong, Zhu Xingyu, Song Penghua, Li, Xiaolei
出版事項:	MDPI AG
主題:	Accuracy Shallow water Datasets Flow Transmission loss Mapping Computer applications Batch processing Telecommunications Deep learning Prediction models Propagation Computation Acoustics Sound transmission Neural networks Computational efficiency Sound velocity Variables Communications systems Controllability Physical properties Learning Underwater Computing time Waveguides Environmental
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その他の書誌記述
抄録:	Efficient prediction of shallow-water acoustic transmission loss (TL) is crucial for underwater detection, recognition, and communication systems. Traditional physical modeling methods require repeated calculations for each new scenario in practical waveguide environments, leading to low computational efficiency. Deep learning approaches, based on data-driven principles, enable accurate input–output approximation and batch processing of large-scale datasets, significantly reducing computation time and cost. To establish a rapid prediction model mapping sound speed profiles (SSPs) to acoustic TL through controllable generation, this study proposes a hybrid framework that integrates a variational autoencoder (VAE) and a normalizing flow (Flow) through a two-stage training strategy. The VAE network is employed to learn latent representations of TL data on a low-dimensional manifold, while the Flow network is additionally used to establish a bijective mapping between the latent variables and underwater physical parameters, thereby enhancing the controllability of the generation process. Combining the trained normalizing flow with the VAE decoder could establish an end-to-end mapping from SSPs to TL. The results demonstrated that the VAE–Flow network achieved higher computational efficiency, with a computation time of 4 s for generating 1000 acoustic TL samples, versus the over 500 s required by the KRAKEN model, while preserving accuracy, with median structural similarity index measure (SSIM) values over 0.90.
ISSN:	2077-1312
DOI:	10.3390/jmse13071325
ソース:	Engineering Database