| Sound speed profile（SSP） is one of the important elements of marine environmental observation， and the introduction of remote sensing parameters for SSP inversion can obtain sound speed data in real time. The inversion first relies on an accurate basis function for the reduced dimensional representation of the sound speed field. In this paper, we propose a scheme to improve the dimensionality reduction accuracy by using non-linear Learned Dictionaries（LDs）, and use the Self-organizing Map （SOM） algorithm, which performs well in multi-source information fusion, for SSP inversion in the South China Sea. The experimental results were assessed using root mean square error （RMSE） as the accuracy. The experimental results show that the dimensionality reduction accuracy of LDs is improved by 0.13 m/s for third -order basis functions and 0.07 m/s for fifth -order basis functions compared with that of Empirical Orthogonal
Functions (EOFs). The inversion accuracy of 3.01 m/s for LDs using fifth order basis functions is lower than that of 2.47 m/s for EOFs. The inversion accuracy of LDs is lower than that of EOFs because the inversion error is amplified during the machine learning process of training the basis functions, resulting in suboptimal inversion coefficients. Compared with the conventional EOFs, using LDs basis functions, the orthogonality limitation can be effectively broken, the perturbation of sound speed is more accurately represented, a basis function learning scheme for acoustic signal processing tasks is provided. |
