Research on inversion of sound speed profile using dictionary learning method

In view of the problem that the resolution of the estimated value of sound speed profile (SSP) obtained by the empirical orthogonal function (EOF) modeling and inversion is low, the K-singular value decomposition (K-SVD) dictionary learning method is used to generate non-orthogonal atoms of the sound speed profile, and the performance of the learning dictionary (LD) generated by this method in reconstruction of sound speed profile is studied. First, the K-SVD algorithm is used to train the SSP dictionary from the obtained data, then the sparse method of orthogonal matching pursuit (OMP) is used to give the sparse vector of the training signal, and finally the estimated value of SSP is obtained through the obtained optimal learning dictionary and sparse vector inversion. The results show that the K-SVD algorithm uses fewer basis functions than the EOF algorithm to describe the SSP changes well and obtain higher inversion accuracy.