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

Vladimir Mochalov; Anastasia Mochalova

doi:10.1117/12.2540792

18 December 2019 The use of deep learning to highlight the shape of geophysical signals

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available