Affiliation:
1. Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
2. Hanjiang Laboratory, Wuhan 430060, China
3. School of Space Science and Physics, Shandong University at Weihai, Weihai 264209, China
Abstract
Underwater Sound Speed Profile (SSP) distribution is crucial for the propagation mode of acoustic signals, so fast and accurate estimation of SSP is of great importance in building underwater observation systems. The state-of-the-art SSP inversion methods include frameworks of matched field processing (MFP), compressive sensing (CS), and feed-forward neural networks (FNNs), among which the FNN shows better real-time performance while maintaining the same level of accuracy. However, the training of FNN needs quite a lot historical SSP samples, which is difficult to satisfy in many ocean areas. This situation is called few-shot learning. To tackle this issue, we propose a multi-task learning (MTL) model with partial parameter sharing among different training tasks. By MTL, common features could be extracted, which accelerates the learning process on given tasks, and reduces the demand for reference samples, enhancing the generalization ability in few-shot learning. To verify the feasibility and effectiveness of MTL, a deep-ocean experiment was held in April 2023 in the South China Sea. Results show that MTL outperforms the other mainstream methods in terms of accuracy for SSP inversion, while inheriting the real-time advantage of FNN during the inversion stage.
Funder
Natural Science Foundation of Shandong Province
Laoshan Laboratory
China Postdoctoral Science Foundation
Qingdao Postdoctoral Science Foundation
National Natural Science Foundation of China
National Defense Science and Technology Innovation Special Zone Project: Marine Science and Technology Collaborative Innovation Center
Fundamental Research Funds for the Central Universities, Ocean University of China
Subject
General Earth and Planetary Sciences
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