Full-sea Depth Sound Speed Profiles Prediction Using RNN and Attention Mechanism

WANG Tong; SU Lin; REN Qunyan; WANG Wenbo; JIA Yuqing; MA Li

doi:10.11999/JEIT210078

Volume 44 Issue 2

Feb. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(2): 726-736

WANG Tong, SU Lin, REN Qunyan, WANG Wenbo, JIA Yuqing, MA Li. Full-sea Depth Sound Speed Profiles Prediction Using RNN and Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(2): 726-736. doi: 10.11999/JEIT210078

Citation:

WANG Tong, SU Lin, REN Qunyan, WANG Wenbo, JIA Yuqing, MA Li. Full-sea Depth Sound Speed Profiles Prediction Using RNN and Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(2): 726-736. doi: 10.11999/JEIT210078

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Full-sea Depth Sound Speed Profiles Prediction Using RNN and Attention Mechanism

doi: 10.11999/JEIT210078

WANG Tong^{1, 2},
SU Lin^{1
,
,},
REN Qunyan¹,
WANG Wenbo¹,
JIA Yuqing¹,
MA Li¹

1.
Key Laboratory of Underwater Environment, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: the National Natural Science Foundation of China (11704396)

Received Date: 2021-01-21
Accepted Date: 2021-11-05
Rev Recd Date: 2021-10-08

Available Online: 2021-11-18

Publish Date: 2022-02-25

Abstract

Abstract

The Sound Speed Profiles (SSPs) in sea water have obvious time evolution characteristics, and their prediction can be regarded as a nonlinear time series prediction. Recurrent Neural Networks (RNN), a type of deep neural network designed for sequence modeling, can capture nonlinear relationships flexibly. Attention Mechanism (AM), which selects the most critical information for the current task, can describe the nonlinear relationships in space and temporal dimensions. In this paper, RNN and AM are used to construct a multivariate time series prediction model to learn the historical SSPs and predict the time-varying full-sea SSPs in shallow sea environment. Experiments on real sound speed data show that the proposed method is effective and outperforms other methods, and provides a new idea for the combination of physical model and machine learning in underwater acoustics.
- Deep learning,
- Recurrent Neural Networks(RNN),
- Attention Mechanism (AM),
- Sound Speed Profiles (SSP) prediction

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References(30)

References

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