  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.
