  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.
