Underwater Sound Speed Profile (SSP) distribution has great influence on the propagation mode of acoustic signal, thus the 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 feedforeward neural networks (FNN), among which the FNN shows better real-time performance while maintain the same level of accuracy. However, the training of FNN needs quite a lot historical SSP samples, which is diffcult to be satisfied 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 traning tasks. By MTL, common features could be extracted, thus accelerating the learning process on given tasks, and reducing the demand for reference samples, so as to enhance the generalization ability in few-shot learning. To verify the feasibility and effectiveness of MTL, a deep-ocean experiment was held in April 2023 at the South China Sea. Results shows that MTL outperforms the state-of-the-art methods in terms of accuracy for SSP inversion, while inherits the real-time advantage of FNN during the inversion stage.

翻译：水下声速剖面（SSP）分布对声信号的传播模式具有重要影响，因此快速准确地估计SSP对于构建水下观测系统至关重要。当前最先进的SSP反演方法包括匹配场处理（MFP）、压缩感知（CS）和前馈神经网络（FNN）等框架，其中FNN在保持同等精度的同时展现出更优的实时性能。然而，FNN的训练需要大量历史SSP样本，这在许多海域难以满足，该情况被称为小样本学习。为解决这一问题，我们提出一种在训练任务间采用部分参数共享的多任务学习（MTL）模型。通过MTL可提取共同特征，从而加速给定任务的学习过程，降低对参考样本的需求，进而增强小样本学习场景下的泛化能力。为验证MTL的可行性与有效性，2023年4月在南海开展了一项深海实验。结果表明，MTL在SSP反演精度方面优于现有方法，同时在反演阶段保持了FNN的实时性优势。