Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement
Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range current measurement under the Arcti...
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ftdoajarticles:oai:doaj.org/article:107da90c4617491c9049cef0c357b69a 2023-05-15T14:33:13+02:00 Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement Kangkang Jin Jian Xu Zichen Wang Can Lu Long Fan Zhongzheng Li Jiaxin Zhou 2021-07-01T00:00:00Z https://doi.org/10.3390/jmse9070755 https://doaj.org/article/107da90c4617491c9049cef0c357b69a EN eng MDPI AG https://www.mdpi.com/2077-1312/9/7/755 https://doaj.org/toc/2077-1312 doi:10.3390/jmse9070755 2077-1312 https://doaj.org/article/107da90c4617491c9049cef0c357b69a Journal of Marine Science and Engineering, Vol 9, Iss 755, p 755 (2021) ocean acoustic tomography velocity field RLS convolutional neural network acoustic inverse problem Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 article 2021 ftdoajarticles https://doi.org/10.3390/jmse9070755 2022-12-31T10:34:49Z Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range current measurement under the Arctic sea ice. Furthermore, affected by the high latitudes Coriolis force, small-scale variability greatly affects the accuracy of Arctic acoustic tomography. However, small-scale variability could not be measured by empirical parameters and resolved by Regularized Least Squares (RLS) in the inverse problem of Arctic acoustic tomography. In this paper, the convolutional neural network (CNN) is proposed to enhance the prediction accuracy in the Arctic, and especially, Gaussian noise is added to reflect the disturbance of the Arctic environment. First, we use the finite element method to build the background ocean model. Then, the deep learning CNN method constructs the non-linear mapping relationship between the acoustic data and the corresponding flow velocity. Finally, the simulation result shows that the deep learning convolutional neural network method being applied to Arctic acoustic tomography could achieve 45.87% accurate improvement than the common RLS method in the current inversion. Article in Journal/Newspaper Arctic Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Journal of Marine Science and Engineering 9 7 755 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
ocean acoustic tomography velocity field RLS convolutional neural network acoustic inverse problem Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 |
spellingShingle |
ocean acoustic tomography velocity field RLS convolutional neural network acoustic inverse problem Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Kangkang Jin Jian Xu Zichen Wang Can Lu Long Fan Zhongzheng Li Jiaxin Zhou Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement |
topic_facet |
ocean acoustic tomography velocity field RLS convolutional neural network acoustic inverse problem Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 |
description |
Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range current measurement under the Arctic sea ice. Furthermore, affected by the high latitudes Coriolis force, small-scale variability greatly affects the accuracy of Arctic acoustic tomography. However, small-scale variability could not be measured by empirical parameters and resolved by Regularized Least Squares (RLS) in the inverse problem of Arctic acoustic tomography. In this paper, the convolutional neural network (CNN) is proposed to enhance the prediction accuracy in the Arctic, and especially, Gaussian noise is added to reflect the disturbance of the Arctic environment. First, we use the finite element method to build the background ocean model. Then, the deep learning CNN method constructs the non-linear mapping relationship between the acoustic data and the corresponding flow velocity. Finally, the simulation result shows that the deep learning convolutional neural network method being applied to Arctic acoustic tomography could achieve 45.87% accurate improvement than the common RLS method in the current inversion. |
format |
Article in Journal/Newspaper |
author |
Kangkang Jin Jian Xu Zichen Wang Can Lu Long Fan Zhongzheng Li Jiaxin Zhou |
author_facet |
Kangkang Jin Jian Xu Zichen Wang Can Lu Long Fan Zhongzheng Li Jiaxin Zhou |
author_sort |
Kangkang Jin |
title |
Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement |
title_short |
Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement |
title_full |
Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement |
title_fullStr |
Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement |
title_full_unstemmed |
Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement |
title_sort |
deep learning convolutional neural network applying for the arctic acoustic tomography current inversion accuracy improvement |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/jmse9070755 https://doaj.org/article/107da90c4617491c9049cef0c357b69a |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Journal of Marine Science and Engineering, Vol 9, Iss 755, p 755 (2021) |
op_relation |
https://www.mdpi.com/2077-1312/9/7/755 https://doaj.org/toc/2077-1312 doi:10.3390/jmse9070755 2077-1312 https://doaj.org/article/107da90c4617491c9049cef0c357b69a |
op_doi |
https://doi.org/10.3390/jmse9070755 |
container_title |
Journal of Marine Science and Engineering |
container_volume |
9 |
container_issue |
7 |
container_start_page |
755 |
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1766306490857029632 |