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18 December 2019 The use of deep learning to highlight the shape of geophysical signals
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Proceedings Volume 11208, 25th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics; 112080I (2019) https://doi.org/10.1117/12.2540792
Event: XXV International Symposium, Atmospheric and Ocean Optics, Atmospheric Physics, 2019, Novosibirsk, Russian Federation
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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vladimir Mochalov and Anastasia Mochalova "The use of deep learning to highlight the shape of geophysical signals", Proc. SPIE 11208, 25th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 112080I (18 December 2019); https://doi.org/10.1117/12.2540792
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