Deep Learning Convolutional Neural Network applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement.
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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 relation.....
JournalJournal of Marine Science and Engineering
Sustainable Development Goals (SDG)14.a
Maturity LevelPilot or Demonstrated
Spatial CoverageArctic Region
CitationJin, K., Xu, J., Wang, Z., Lu, C., Fan, L., Li, Z. and Zhou, J. (2021) Deep Learning Convolutional Neural Network applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement. Journal of Marine Science and Engineering, 9:755, 15pp. DOI: https://doi.org/10.3390/jmse9070755
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