Compression AutoEncoder for High-Resolution Ocean Sound Speed Profile Data

Abstract

High-resolution ocean sound speed profile (HROSSP) data is essential for ocean acoustic modeling and sonar performance evaluation. However, the large volume and storage requirements of this data severely restrict its practical application in ocean acoustics. In this paper, we propose a compression autoencoder specifically designed for managing HROSSP data (CAE-HROSSP) and investigate the optimal network structure. Experimental results demonstrate that by using the min-max normalization method for input data and the corresponding inverse normalization for output data, along with employing the LeakyReLU function as the final activation layer, the accuracy of decompressed data reconstruction can be significantly improved. To tackle the challenges of fitting the distribution of surface sound speed data caused by significant variations and noise, we propose two loss functions: slice mean square error and elemental mean square error. These loss functions are combined with mean squared error through weighted summation to enhance CAE-HROSSP’s ability to fit the distribution of surface sound speed values and minimize the reconstruction errors of compressed data. Performance evaluation experiments reveal that CAE-HROSSP outperforms two existing methods in compressing HROSSP data, achieving superior performance with smaller data reconstruction errors at higher compression ratios. Furthermore, transfer learning is utilized to enhance the training of CAE-HROSSP, employing HROSSP data from the area where the mesoscale eddy is situated, as well as at the convergence of cold and warm ocean currents. The compression performance of both the training set and the validation set is comparable in the sea, where the structure of the sound speed profile varies greatly. This indicates that CAE-HROSSP can compress highly variable sound speed profile data in more sea areas using transfer learning, and has the potential to be extended globally. The findings and insights obtained from this study provide guidance for future endeavors in utilizing autoencoders to compress HROSSP data.