Estimation of sonic layer depth from surface parameters

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Abstract

[1] Sonic layer depth (SLD), an important parameter in underwater acoustics, is the near surface depth of first maxima of the sound speed in the ocean. The lack of direct observations of vertical profiles of velocimeters or temperature and salinity, from which sound speed and SLD can be calculated, hampers the investigation of SLD. In this study, we demonstrate SLD estimation using artificial neural network (ANN) from surface measurements that can be replaced with satellite observations later. Surface and subsurface measurements from a central Arabian Sea mooring are used for this purpose. The estimated SLD had a root mean square error (correlation coefficient) of 11.83 m (0.84). Approximately 76% (91%) of estimations lie within ±10 m (±20 m). SLD has also been estimated from surface parameters using multiple regression technique (MRT). ANN proved its superiority over MRT in estimating SLD from surface parameters.

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