Dictionary learning of sound speed profiles

TL;DR

This paper demonstrates that dictionary learning can significantly enhance the resolution of ocean sound speed profile estimates by generating adaptable shape functions that require fewer coefficients than traditional EOF methods.

Contribution

It introduces the use of dictionary learning, specifically the K-SVD algorithm, for SSP modeling, surpassing EOF-based regularization in resolution and efficiency.

Findings

LDs better explain SSP variability than EOFs

LDs require fewer coefficients, often just one per profile

LDs improve SSP resolution with negligible computational cost

Abstract

To provide constraints on their inversion, ocean sound speed profiles (SSPs) often are modeled using empirical orthogonal functions (EOFs). However, this regularization, which uses the leading order EOFs with a minimum-energy constraint on their coefficients, often yields low resolution SSP estimates. In this paper, it is shown that dictionary learning, a form of unsupervised machine learning, can improve SSP resolution by generating a dictionary of shape functions for sparse processing (e.g. compressive sensing) that optimally compress SSPs; both minimizing the reconstruction error and the number of coefficients. These learned dictionaries (LDs) are not constrained to be orthogonal and thus, fit the given signals such that each signal example is approximated using few LD entries. Here, LDs describing SSP observations from the HF-97 experiment and the South China Sea are generated using the K-SVD algorithm. These LDs better explain SSP variability and require fewer coefficients than EOFs, describing much of the variability with one coefficient. Thus, LDs improve the resolution of SSP estimates with negligible computational burden.