Inversion of the Full-Depth Temperature Profile Based on Few Depth-Fixed Temperatures

Abstract

Seawater temperature plays a key role in underwater acoustics and marine fishery, etc. In oceanographic surveys, it is often desirable to detect the temperature profile and obtain its spatio-temporal variation. The present study shows that the temperatures at the depths which are the three extreme points of the first two empirical orthogonal function (EOF) modes, contain the largest amount of information. Based on the back propagation (BP) neural network, a model for reconstructing the full-depth temperature profile using a few temperatures at fixed depth is established. The experimental result shows that the root mean square error (RMSE) of the temperature profile inversion in the test set is mostly less than 0.2 °C, and the three-dimensional temperature field obtained in this study is relatively reliable.