Shallow-Water Geoacoustic Parameter Inversion with Stokes Parameters and a Multi-Task Attention U-Net

HUANG Qianzhuo; LI Xiaoman; BI Xuejie; ZHANG Zishi; TONG Han; LI Fei

doi:10.11999/JEIT251085

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HUANG Qianzhuo, LI Xiaoman, BI Xuejie, ZHANG Zishi, TONG Han, LI Fei. Shallow-Water Geoacoustic Parameter Inversion with Stokes Parameters and a Multi-Task Attention U-Net[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251085

Citation:

HUANG Qianzhuo, LI Xiaoman, BI Xuejie, ZHANG Zishi, TONG Han, LI Fei. Shallow-Water Geoacoustic Parameter Inversion with Stokes Parameters and a Multi-Task Attention U-Net[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251085

Citation:

HUANG Qianzhuo, LI Xiaoman, BI Xuejie, ZHANG Zishi, TONG Han, LI Fei. Shallow-Water Geoacoustic Parameter Inversion with Stokes Parameters and a Multi-Task Attention U-Net[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251085

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Shallow-Water Geoacoustic Parameter Inversion with Stokes Parameters and a Multi-Task Attention U-Net

doi: 10.11999/JEIT251085 cstr: 32379.14.JEIT251085

Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Funds: The National Natural Science Foundation of China (12204199, 12204200)

Received Date: 2025-10-13
Accepted Date: 2026-03-09
Rev Recd Date: 2026-03-08

Available Online: 2026-03-22

Abstract

Abstract

Objective Geoacoustic parameters in shallow water are crucial for analyzing the characteristics of underwater acoustic propagation. However, traditional inversion methods face challenges such as high computational complexity, significant cost, and strong dependence on the accuracy of environmental models. To address these issues, this study proposes an efficient and robust inversion approach designed to overcome the limitations of conventional methods. The proposed method aims to provide more reliable and stable estimation of shallow-water geoacoustic parameters, enabling improved performance in practical applications while maintaining computational efficiency and robustness. Methods This study is based on the Stokes polarization parameters of the vector acoustic field. Signals received by a single vector hydrophone are processed using a warping transformation to separate and extract normal modes propagating in a shallow-water waveguide. The extracted signals are subsequently used to compute the Stokes parameters, which are normalized and employed as input features for the inversion model. An attention-enhanced multi-task U-Net neural network is constructed, adopting a shared encoder and multiple decoder branches to predict key geoacoustic parameters, including compressional wave velocity, shear wave velocity, density, and attenuation coefficients. In addition, channel and spatial attention mechanisms, together with a multi-task loss function incorporating uncertainty weighting, are applied to optimize feature extraction and achieve adaptive balancing among the different parameter inversion tasks. Results and Discussions The introduced attention mechanism proves effective in suppressing fluctuations in model predictions, thereby significantly boosting the accuracy and stability of geoacoustic parameter inversion.The mean absolute percentage errors for both compressional and shear wave velocities were consistently below 5 %(Table 2) upon evaluation on a dataset comprising 200 test cases. With the incorporation of attention mechanisms, the errors in shear wave velocity and seabed density were further reduced to less than 3 %(Table 3), demonstrating enhanced precision in predicting these key parameters. The proposed method is not only insensitive to parameters mismatch but also exhibits strong robustness against environmental variations. Furthermore, the approach was validated using real measurement data from a shallow-water region in the northern South China Sea(Fig.16), confirming both the effectiveness and reliability of the method in practical scenarios(Table 4 and Fig.18). These results collectively demonstrate that the attention-enhanced multi-task U-Net framework can effectively capture critical features from Stokes polarization parameters, leading to more stable and accurate geoacoustic parameter estimation in shallow-water environments. Conclusions The inversion method based on Stokes parameters and an attention-enhanced multi-task U-Net can effectively improve the accuracy and stability of shallow-water geoacoustic parameter estimation, showing particularly strong performance in predicting compressional wave velocity, shear wave velocity, and density. However, it still has limitations in the inversion of seabed attenuation coefficients. Future research should further improve feature extraction methods and network architecture, and explore the applicability of this approach under more complex marine conditions.
- Vector acoustic field,
- Stokes parameters,
- Geoacoustic parameter inversion,
- U-Net neural network,
- Attention mechanism

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References(21)

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