Striking The Right Balance: Three-Dimensional Ocean Sound Speed Field Reconstruction Using Tensor Neural Networks

TL;DR

This paper introduces a tensor neural network tailored for 3D ocean sound speed field reconstruction, effectively balancing expressiveness and noise rejection, and demonstrating superior performance over existing methods on real data.

Contribution

A novel tensor neural network model specifically designed for 3D ocean sound speed field reconstruction, integrating tensor computations with deep learning for improved accuracy.

Findings

Outperforms state-of-the-art methods on South China Sea data

Effectively rejects noise in 3D SSF reconstruction

Provides a balanced model combining expressiveness and conciseness

Abstract

Accurately reconstructing a three-dimensional ocean sound speed field (3D SSF) is essential for various ocean acoustic applications, but the sparsity and uncertainty of sound speed samples across a vast ocean region make it a challenging task. To tackle this challenge, a large body of reconstruction methods has been developed, including spline interpolation, matrix/tensor-based completion, and deep neural networks-based reconstruction. However, a principled analysis of their effectiveness in 3D SSF reconstruction is still lacking. This paper performs a thorough analysis of the reconstruction error and highlights the need for a balanced representation model that integrates both expressiveness and conciseness. To meet this requirement, a 3D SSF-tailored tensor deep neural network is proposed, which utilizes tensor computations and deep neural network architectures to achieve remarkable 3D SSF reconstruction. The proposed model not only includes the previous tensor-based SSF representation model as a special case, but also has a natural ability to reject noise. The numerical results using the South China Sea 3D SSF data demonstrate that the proposed method outperforms state-of-the-art methods. The code is available at https://github.com/OceanSTARLab/Tensor-Neural-Network.