Reconstructing Sound Speed Profile From Remote Sensing Data: Nonlinear Inversion Based on Self-Organizing Map

Haipeng Li,Jianbo Zhou,Ke Qu

doi:10.1109/access.2021.3102608

Abstract

By establishing a linear regression relationship between the projection coefficient of the empirical orthogonal function (EOF) of the sound speed profile (SSP) and remote sensing parameters of the sea surface, the single empirical orthogonal function regression (sEOF-r) method was used to reconstruct the underwater SSP from satellite remote sensing data. However, because the ocean is a complex dynamical system, the parameters of the surface and the subsurface did not conform to the linear regression model in strict sense. This paper proposes a self-organizing map (SOM)-based nonlinear inversion method that used satellite observations to obtain anomalies in data on the sea surface temperature and height, and combined them with the EOF coefficient from an Argo buoy to train and generate a map. The SSP was then reconstructed by obtaining the best matching neuron. The results of SSP reconstruction in the northern part of the South China Sea showed that the relationship between the parameters of the sea surface and the subsurface could be adequately expressed by the nonlinear neuronal topology. The SOM algorithm generated a smaller inversion error than linear inversion and had better robustness. It improved the average accuracy of reconstruction by 0.88 m/s and reduced the mean-squared reconstruction error to less than 1.19 m/s. It thus offered significant promise for acoustic applications.

Highlights

A S the basic physical parameter used to describe the acoustic characteristics of water columns, the sound speed profile (SSP) is crucial to applications of marine acoustics, such as underwater target recognition [1], marine environmental monitoring [2], and underwater communication [3]
As the linear regression was based on a large number of samples, differences were obtained between individual and statistical characteristics that led to uncertainty in inversion using single empirical orthogonal function regression (sEOF-r), especially in the experimental sea area that featured a strong water transport
In this paper, a nonlinear inversion method based on selforganizing map is proposed

Summary

INTRODUCTION

A S the basic physical parameter used to describe the acoustic characteristics of water columns, the sound speed profile (SSP) is crucial to applications of marine acoustics, such as underwater target recognition [1], marine environmental monitoring [2], and underwater communication [3]. With recent developments in observation technologies, such as Argo and remote sensing, data samples of the underwater profile and ocean surface are being continually accumulated, and a strong relevance has been noted between the underwater profiles and remote sensing data This has been used to obtain empirical relations between the surface and subsurface parameters at sea, and for the real-time remote sensing-based monitoring of underwater profiles [4]–[11]. Munk and Wunsch proposed a method for the large-scale inversion of the SSP by using acoustic rays time differences and used this to observe global water temperatures perturbation [12]. In the framework of the sEOF-r, MODAS provides a dynamic climatology that can obtain data on the height and temperature of the sea surface to predict underwater structures by constructing synthetic profiles generated by regression analysis [6]. Compared with the sEOR-r method, the results of SSP reconstruction obtained have a smaller error and better robustness, which improves the accuracy and applicability of the method of SSP inversion using parameters of remote sensing

METHODOLOGY

EOF ANALYSIS

SSP RECONSTRUCTION

Findings

CONCLUSION