Small-scale Ocean Acoustic Tomography

1997 ◽  
Author(s):  
Jerald W. Caruthers
Keyword(s):  
Small Scale ◽  
Acoustic Tomography ◽  
Ocean Acoustic Tomography

Application of multilayered neural networks to ocean acoustic tomography inversions

1996 ◽  
Vol 3 (4) ◽  
pp. 281-304 ◽  
Author(s):  
Y. Stéphan ◽  
S. Thiria ◽  
F. Badran
Keyword(s):  
Neural Networks ◽  
Acoustic Tomography ◽  
Ocean Acoustic Tomography

scholarly journals Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement

2021 ◽  
Vol 9 (7) ◽  
pp. 755
Author(s):  
Kangkang Jin ◽  
Jian Xu ◽  
Zichen Wang ◽  
Can Lu ◽  
Long Fan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sea Ice ◽  
Convolutional Neural Network ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Small Scale ◽  
Acoustic Tomography ◽  
Arctic Sea ◽  
Current Inversion

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.

Optimum design of the ocean acoustic tomography system for the Sea of Japan

1994 ◽  
Vol 50 (3) ◽  
pp. 281-293 ◽  
Author(s):  
Arata Kaneko ◽  
Gang Yuan ◽  
Noriaki Gohda ◽  
Iwao Nakano
Keyword(s):  
Optimum Design ◽  
Sea Of Japan ◽  
Acoustic Tomography ◽  
The Sea Of Japan ◽  
Tomography System ◽  
Ocean Acoustic Tomography

Matched Field Ocean Acoustic Tomography Using Genetic Algorithms

1996 ◽  
pp. 601-606
Author(s):  
Michael I. Taroudakis ◽  
Maria G. Markaki
Keyword(s):  
Genetic Algorithms ◽  
Acoustic Tomography ◽  
Ocean Acoustic Tomography

Ocean acoustic tomography

Endeavour ◽  
1996 ◽  
Vol 20 (1) ◽  
pp. 46
Author(s):  
Anton Edwards
Keyword(s):  
Acoustic Tomography ◽  
Ocean Acoustic Tomography
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