Source depth estimation using spectral transformations and convolutional neural network in a deep-sea environment

Abstract

Multiple approaches for depth estimation in deep-ocean environments are discussed. First, a multispectral transformation for depth estimation (MSTDE) method based on the low-spatial-frequency interference in a constant sound speed is derived to estimate the source depth directly. To overcome the limitation of real sound-speed profiles and source bandwidths on the accuracy of MSTDE, a method based on a convolution neural network (CNN) and conventional beamforming (CBF) preprocessing is proposed. Further, transfer learning is adapted to tackle the effect of noise on the estimation result. At-sea data are used to test the performance of these methods, and results suggest that (1) the MSTDE can estimate the depth; however, the error increases with distance; (2) MSTDE error can be moderately compensated through a calculated factor; (3) the performance of deep-learning approach using CBF preprocessing is much better than those of MSTDE and traditional CNN.