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Estimation of sound speed profiles based on remote sensing parameters using a scalable end-to-end tree boosting model

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العنوان: Estimation of sound speed profiles based on remote sensing parameters using a scalable end-to-end tree boosting model
المؤلفون: Zhenyi Ou, Ke Qu, Min Shi, Yafen Wang, Jianbo Zhou
المصدر: Frontiers in Marine Science, Vol 9 (2022)
بيانات النشر: Frontiers Media S.A., 2022.
سنة النشر: 2022
المجموعة: LCC:Science
LCC:General. Including nature conservation, geographical distribution
مصطلحات موضوعية: sound speed profile, remote sensing data, XGBoost, Argo profiles, the South China Sea, Science, General. Including nature conservation, geographical distribution, QH1-199.5
الوصف: IntroductionIn underwater acoustic applications, the three-dimensional sound speed distribution has a significant impact on signal propagation. However, the traditional sound speed profile (SSP) measurement method requires a lot of manpower and time, and it is difficult to popularize. Satellite remote sensing can collect information on a large ocean surface area, from which the underwater information can be derived.MethodIn this paper, we propose a method for reconstructing the SSP based on an extensible end-to-end tree boosting (XGBoost) model. Combined with satellite remote sensing data and Argo profile data, it extracts the characteristic matrix of the SSP and analyzes the contribution rate of each order matrix to reduce the introduction of noise. The model inverts the SSP above 1000 m in the South China Sea by using the root mean square error (RMSE) as the precision evaluation index.ResultThe results showed that the XGBoost model could better reconstruct the SSP above 1000 m, with a RMSE of 1.75 m/s. Compared with the single empirical orthogonal function regression (sEOF-r) model of the linear regression method, the RMSE of the XGBoost model was reduced by 0.59 m/s.DiscussionFor this model, the RMSE of the inversion results was smaller, the robustness was better, and the regression performance was superior to that of the sEOF-r model at different depths. This study provided an efficient tree boosting model for SSP reconstruction, which could reliably and instantaneously monitor the 3D sound speed distribution.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2296-7745
Relation: https://www.frontiersin.org/articles/10.3389/fmars.2022.1051820/full; https://doaj.org/toc/2296-7745
DOI: 10.3389/fmars.2022.1051820
URL الوصول: https://doaj.org/article/bb4fc5e99ee34f4cab0eef69ef344fe1
رقم الأكسشن: edsdoj.bb4fc5e99ee34f4cab0eef69ef344fe1
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:22967745
DOI:10.3389/fmars.2022.1051820