Abstract
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.
Abstract (translated)
水下声速剖面（SSP）分布对声波传播模式有很大的影响，因此快速和准确地估计SSP对构建水下观测系统非常重要。最先进的SSP反演方法包括匹配场处理（MFP）框架、压缩感知（CS）和前馈神经网络（FNN）等。其中，FNN在保持相同准确性的同时具有更好的实时性能。然而，为了训练FNN，需要相当多的历史SSP样本，这在许多海洋区域中是难以满足的。这种情况称为欠样本学习。为了解决这个问题，我们提出了一个多任务学习（MTL）模型，其中不同训练任务之间共享部分参数。通过MTL，可以提取共同特征，从而加速在给定任务上的学习过程，并减少对参考样本的需求，从而增强在欠样本学习中的泛化能力。为了验证MTL的可行性和有效性，2023年4月在南海进行了一个深海实验。结果表明，MTL在SSP反演方面的准确性超过了最先进的方法，而在反演阶段，FNN具有实时优势。
URL
https://arxiv.org/abs/2310.11708
