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The use of deep learning to highlight the shape of geophysical signals
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Paper Abstract

During analyzing of geophysical data, the problem of highlighting a form of geophysical signals often appears. In this work, it is proposed to use deep learning, which is currently one of the top priorities in the field of artificial intelligence and machine learning. The samples of geophysical signals, as well as the generated samples of signals by their mathematical models and typical examples of forms, act as a training dataset for deep neural network. End-to-end demonstration examples of the highlighting of reflection traces from different layers of the ionosphere in the ionograms, as well as the highlighting of whistler forms in the VLF spectrograms are presented.

Paper Details

Date Published: 18 December 2019
PDF: 4 pages
Proc. SPIE 11208, 25th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 112080I (18 December 2019); doi: 10.1117/12.2540792
Show Author Affiliations
Vladimir Mochalov, Institute of Cosmophysical Researches and Radio Wave Propagation (Russian Federation)
Anastasia Mochalova, Institute of Cosmophysical Researches and Radio Wave Propagation (Russian Federation)


Published in SPIE Proceedings Vol. 11208:
25th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics
Oleg A. Romanovskii; Gennadii G. Matvienko, Editor(s)

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