Shallow-Water Geoacoustic Parameter Inversion Using Stokes Parameters and an Attention-Enhanced Multi-Task 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 Using Stokes Parameters and an Attention-Enhanced Multi-Task 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 Using Stokes Parameters and an Attention-Enhanced Multi-Task 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 Using Stokes Parameters and an Attention-Enhanced Multi-Task U-Net[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251085

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Shallow-Water Geoacoustic Parameter Inversion Using Stokes Parameters and an Attention-Enhanced Multi-Task 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
Rev Recd Date: 2026-04-22

Available Online: 2026-05-05

Abstract

Abstract

Objective Geoacoustic parameters in shallow water are critical for characterizing underwater acoustic propagation. Traditional inversion methods, however, are limited by high computational complexity, high cost, and strong dependence on the accuracy of environmental models. To address these issues, an efficient and robust inversion method is proposed to improve the reliability and stability of shallow-water geoacoustic parameter estimation while preserving computational efficiency. Methods This method is developed from the Stokes parameters of the vector acoustic field. Signals received by a single vector hydrophone are processed with a warping transform to separate and extract the normal modes propagating in a shallow-water waveguide. The extracted signals are then used to calculate the Stokes parameters, which are normalized and used as input features for the inversion model. An attention-enhanced multi-task U-Net is constructed with a shared encoder and multiple prediction branches to estimate key geoacoustic parameters, including compressional wave velocity, shear wave velocity, density, compressional wave attenuation, and shear wave attenuation. In addition, channel attention and spatial attention, together with a multi-task loss function with uncertainty weighting, are used to improve feature extraction and adaptively balance the different parameter inversion tasks. Results and Discussions The attention mechanism is shown to suppress fluctuations in model predictions and to improve the accuracy and stability of geoacoustic parameter inversion. When 200 test samples are evaluated, the mean absolute percentage errors of both compressional wave velocity and seabed density remain below 5% (Table 3). After the attention mechanism is introduced, the errors in compressional wave velocity and seabed density are further reduced to below 3% (Table 5), which indicates improved prediction accuracy for these key parameters. The proposed method is also shown to be insensitive to parameter mismatch and to have strong robustness to environmental variation. Furthermore, the method is validated with measured data from a shallow-water region in the northern South China Sea, and its effectiveness and reliability in practical applications are confirmed (Table 6 and Fig. 9). These results show that the attention-enhanced multi-task U-Net effectively captures critical features from the Stokes parameters and yields more stable and accurate geoacoustic parameter estimation in shallow-water environments. Conclusions The inversion method based on the Stokes parameters and an attention-enhanced multi-task U-Net effectively improves the accuracy and stability of shallow-water geoacoustic parameter estimation and shows strong performance in the prediction of compressional wave velocity, shear wave velocity, and density. However, limitations remain in the inversion of seabed attenuation. Future work should focus on improving feature extraction methods and network architecture and on testing the applicability of the method 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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