Rapid environmental assessment in the South China Sea: Improved inversion of sound speed profile using remote sensing data

Ke Qu; Binbin Zou; Jianbo Zhou

doi:10.1007/s13131-022-2032-2

Volume 41 Issue 7

Jul. 2022

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Ke Qu, Binbin Zou, Jianbo Zhou. Rapid environmental assessment in the South China Sea: Improved inversion of sound speed profile using remote sensing data[J]. Acta Oceanologica Sinica, 2022, 41(7): 78-83. doi: 10.1007/s13131-022-2032-2

Citation:

Ke Qu, Binbin Zou, Jianbo Zhou. Rapid environmental assessment in the South China Sea: Improved inversion of sound speed profile using remote sensing data[J]. Acta Oceanologica Sinica, 2022, 41(7): 78-83. doi: 10.1007/s13131-022-2032-2

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Rapid environmental assessment in the South China Sea: Improved inversion of sound speed profile using remote sensing data

doi: 10.1007/s13131-022-2032-2

Ke Qu¹,
Binbin Zou^{2
,
,},
Jianbo Zhou³

1.
College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
2.
Shanghai Acoustics Laboratory, Chinese Academy of Sciences, Shanghai 201815, China
3.
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Funds: The Natural Science Foundation of Guangdong Province under contract No. 2022A1515011519; the National Natural Science Foundation of China under contract No. 11904290.

More Information

Corresponding author: E-mail: zoubb@mail.ioa.ac.cn
Received Date: 2021-06-12
Accepted Date: 2022-04-21

Available Online: 2022-05-13

Publish Date: 2022-07-08

Abstract

Abstract

Complex perturbations in the profile and the sparsity of samples often limit the validity of rapid environmental assessment (REA) in the South China Sea (SCS). In this paper, the remote sensing data were used to estimate sound speed profile (SSP) with the self-organizing map (SOM) method in the SCS. First, the consistency of the empirical orthogonal functions was examined by using k-means clustering. The clustering results indicated that SSPs in the SCS have a similar perturbation nature, which means the inverted grid could be expanded to the entire SCS to deal with the problem of sparsity of the samples without statistical improbability. Second, a machine learning method was proposed that took advantage of the topological structure of SOM to significantly improve their accuracy. Validation revealed promising results, with a mean reconstruction error of 1.26 m/s, which is 1.16 m/s smaller than the traditional single empirical orthogonal function regression (sEOF-r) method. By violating the constraints of linear inversion, the topological structure of the SOM method showed a smaller error and better robustness in the SSP estimation. The improvements to enhance the accuracy and robustness of REA in the SCS were offered. These results suggested a potential utilization of REA in the SCS based on satellite data and provided a new approach for SSP estimation derived from sea surface data.
- South China Sea,
- sound speed profile empirical orthogonal function,
- self-organizing maps

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References(18)

References

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