Automatic Detection of Atmospherics and Tweek Atmospherics in Radio Spectrograms Based on a Deep Learning Approach

Abstract Lightning strikes can be routinely observed by the radio receivers that measure electromagnetic signals in the very low frequency range. The acquired pulses called atmospherics provide valuable details about source lightning discharges and also about the Earth‐ionosphere waveguide where the...

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Autores principales:	Viera Maslej‐Krešňáková, Adrián Kundrát, Šimon Mackovjak, Peter Butka, Samuel Jaščur, Ivana Kolmašová, Ondřej Santolík
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	atmospherics tweeks ionosphere neural networks deep learning Astronomy QB1-991 Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/75aa3ccdddf640f1aefce6f4a9a6c011
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id	oai:doaj.org-article:75aa3ccdddf640f1aefce6f4a9a6c011
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spelling	oai:doaj.org-article:75aa3ccdddf640f1aefce6f4a9a6c0112021-11-23T21:03:07ZAutomatic Detection of Atmospherics and Tweek Atmospherics in Radio Spectrograms Based on a Deep Learning Approach2333-508410.1029/2021EA002007https://doaj.org/article/75aa3ccdddf640f1aefce6f4a9a6c0112021-11-01T00:00:00Zhttps://doi.org/10.1029/2021EA002007https://doaj.org/toc/2333-5084Abstract Lightning strikes can be routinely observed by the radio receivers that measure electromagnetic signals in the very low frequency range. The acquired pulses called atmospherics provide valuable details about source lightning discharges and also about the Earth‐ionosphere waveguide where they can propagate thousands of kilometers. The automatic acquisition of the events requires also automatic methods for extraction of atmospherics' details to confirm the observed trends with statistical significance. For this purpose, we have developed a method based on a deep learning approach to automatically obtain the type of atmospherics, their exact time, and the frequency range from the frequency‐time spectrograms. The method that employs convolutional neural networks is designed to be adaptable to scientific needs and provide reliable results according to the requirements on the sensitivity to events, computation performance, and precision of extracted details. The efficiency and specific steps of our method are demonstrated for data recorded by the ELMAVAN‐G instrument. The comprehensive description of the method allows its usage for regular characterization of the ionospheric D‐layer or for other similar applications in the future.Viera Maslej‐KrešňákováAdrián KundrátŠimon MackovjakPeter ButkaSamuel JaščurIvana KolmašováOndřej SantolíkAmerican Geophysical Union (AGU)articleatmosphericstweeksionosphereneural networksdeep learningAstronomyQB1-991GeologyQE1-996.5ENEarth and Space Science, Vol 8, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	atmospherics tweeks ionosphere neural networks deep learning Astronomy QB1-991 Geology QE1-996.5
spellingShingle	atmospherics tweeks ionosphere neural networks deep learning Astronomy QB1-991 Geology QE1-996.5 Viera Maslej‐Krešňáková Adrián Kundrát Šimon Mackovjak Peter Butka Samuel Jaščur Ivana Kolmašová Ondřej Santolík Automatic Detection of Atmospherics and Tweek Atmospherics in Radio Spectrograms Based on a Deep Learning Approach
description	Abstract Lightning strikes can be routinely observed by the radio receivers that measure electromagnetic signals in the very low frequency range. The acquired pulses called atmospherics provide valuable details about source lightning discharges and also about the Earth‐ionosphere waveguide where they can propagate thousands of kilometers. The automatic acquisition of the events requires also automatic methods for extraction of atmospherics' details to confirm the observed trends with statistical significance. For this purpose, we have developed a method based on a deep learning approach to automatically obtain the type of atmospherics, their exact time, and the frequency range from the frequency‐time spectrograms. The method that employs convolutional neural networks is designed to be adaptable to scientific needs and provide reliable results according to the requirements on the sensitivity to events, computation performance, and precision of extracted details. The efficiency and specific steps of our method are demonstrated for data recorded by the ELMAVAN‐G instrument. The comprehensive description of the method allows its usage for regular characterization of the ionospheric D‐layer or for other similar applications in the future.
format	article
author	Viera Maslej‐Krešňáková Adrián Kundrát Šimon Mackovjak Peter Butka Samuel Jaščur Ivana Kolmašová Ondřej Santolík
author_facet	Viera Maslej‐Krešňáková Adrián Kundrát Šimon Mackovjak Peter Butka Samuel Jaščur Ivana Kolmašová Ondřej Santolík
author_sort	Viera Maslej‐Krešňáková
title	Automatic Detection of Atmospherics and Tweek Atmospherics in Radio Spectrograms Based on a Deep Learning Approach
title_short	Automatic Detection of Atmospherics and Tweek Atmospherics in Radio Spectrograms Based on a Deep Learning Approach
title_full	Automatic Detection of Atmospherics and Tweek Atmospherics in Radio Spectrograms Based on a Deep Learning Approach
title_fullStr	Automatic Detection of Atmospherics and Tweek Atmospherics in Radio Spectrograms Based on a Deep Learning Approach
title_full_unstemmed	Automatic Detection of Atmospherics and Tweek Atmospherics in Radio Spectrograms Based on a Deep Learning Approach
title_sort	automatic detection of atmospherics and tweek atmospherics in radio spectrograms based on a deep learning approach
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/75aa3ccdddf640f1aefce6f4a9a6c011
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Automatic Detection of Atmospherics and Tweek Atmospherics in Radio Spectrograms Based on a Deep Learning Approach

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