AUTHOR=Liu Chen , Qu Ke 

TITLE=Wide-area sound speed profile estimation based on a pre-classification scheme for sound speed perturbation modes

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2023.1130061

DOI=10.3389/fmars.2023.1130061

ISSN=2296-7745

ABSTRACT=The trend of sound speed profile (SSP) inversion is towards wide-area sound speed estimation. Traditional techniques divide the sea into latitude and longitude grid cells before inversion. The limitations of this approach are that too large a span of the grid and the inverse basis functions will not be statistically uniform, resulting in a significant reduction in inversion accuracy. However, too small a span will result in insufficient samples to perform the inversion. Thus, this paper proposes a grid-free pre-classification inversion scheme based on empirical orthogonal function (EOF) vectors. The scheme is based on the k-means clustering algorithm (K-means) to classify the samples according to the perturbation mode amplitude of the SSP. After classification, the SSP inversion is carried out using the self-organizing map algorithm (SOM). The experimental sea area is selected from the South China Sea, and the inversion results are evaluated using root mean square error (RMSE) as the criterion. The inversion results show that the inversion error is 2.1 m/s for the pre-classification solution and 2.7 m/s for the solution without pre-classification, a steady improvement of more than 20% in the inversion error. Accuracy is also improved by 2.14 m/s in the depth range where the sound speed perturbance is greatest. The time and space distribution of clustering is analyzed and the sample's geographical location and the time series distribution characteristics reveals that this pre-classification scheme can reasonably and effectively classify samples regardless of time and space constraints. It effectively solves the problem of dividing training grids into complex, insufficiently data-sampled seas, realizing a de-gridded SSP high-precision inversion.