Monitoring space weather: using automated, accurate neural network based whistler segmentation for whistler inversion
It is challenging, yet important, to measure the - ever-changing - cold electron density in the plasmasphere. The cold electron density inside and outside of the plasmapause is a key parameter for radiation belt dynamics. One indirect measurement is through finding the velocity dispersion relation e...
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Main Authors: | , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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American Geophysical Union
2022
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Online Access: | http://nora.nerc.ac.uk/id/eprint/531929/ https://nora.nerc.ac.uk/id/eprint/531929/1/Space%20Weather%20-%202022%20-%20Pataki%20-%20Monitoring%20Space%20Weather%20Using%20Automated%20Accurate%20Neural%20Network%20Based%20Whistler.pdf https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021SW002981 |
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ftnerc:oai:nora.nerc.ac.uk:531929 2023-05-15T13:41:46+02:00 Monitoring space weather: using automated, accurate neural network based whistler segmentation for whistler inversion Pataki, Bálint Ármin Lichtenberger, János Clilverd, Mark Máthé, Gergely Steinbach, Péter Pásztor, Szilárd Murár‐Juhász, Lilla Koronczay, Dávid Ferencz, Orsolya Csabai, István 2022-02-16 text http://nora.nerc.ac.uk/id/eprint/531929/ https://nora.nerc.ac.uk/id/eprint/531929/1/Space%20Weather%20-%202022%20-%20Pataki%20-%20Monitoring%20Space%20Weather%20Using%20Automated%20Accurate%20Neural%20Network%20Based%20Whistler.pdf https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021SW002981 en eng American Geophysical Union https://nora.nerc.ac.uk/id/eprint/531929/1/Space%20Weather%20-%202022%20-%20Pataki%20-%20Monitoring%20Space%20Weather%20Using%20Automated%20Accurate%20Neural%20Network%20Based%20Whistler.pdf Pataki, Bálint Ármin; Lichtenberger, János; Clilverd, Mark orcid:0000-0002-7388-1529 Máthé, Gergely; Steinbach, Péter; Pásztor, Szilárd; Murár‐Juhász, Lilla; Koronczay, Dávid; Ferencz, Orsolya; Csabai, István. 2022 Monitoring space weather: using automated, accurate neural network based whistler segmentation for whistler inversion. Space Weather, 20 (2), e2021SW002981. 12, pp. https://doi.org/10.1029/2021SW002981 <https://doi.org/10.1029/2021SW002981> cc_by_nc_nd_4 CC-BY-NC-ND Publication - Article PeerReviewed 2022 ftnerc https://doi.org/10.1029/2021SW002981 2023-02-04T19:52:59Z It is challenging, yet important, to measure the - ever-changing - cold electron density in the plasmasphere. The cold electron density inside and outside of the plasmapause is a key parameter for radiation belt dynamics. One indirect measurement is through finding the velocity dispersion relation exhibited by lightning induced whistlers. The main difficulty of the method comes from low signal-to-noise ratios for most of the ground-based whistler components. To provide accurate electron density and L-shell measurements, whistler components need to be detectable in the noisy background, and their characteristics need to be reliably determined. For this reason precise segmentation is needed on a spectrogram image. Here we present a fully automated way to perform such an image segmentation by leveraging the power of convolutional neural networks, a state-of-the-art method for computer vision tasks. Testing the proposed method against a manually, and semi-manually segmented whistler dataset achieved <10% relative electron density prediction error for 80% of the segmented whistler traces, while for the L-shell, the relative error is <5% for 90% of the cases. By segmenting more than 1 million additional real whistler traces from Rothera station Antarctica, logged over 9 years, seasonal changes in the average electron density were found. The variations match previously published findings, and confirm the capabilities of the image segmentation technique. Article in Journal/Newspaper Antarc* Antarctica Natural Environment Research Council: NERC Open Research Archive Rothera ENVELOPE(-68.130,-68.130,-67.568,-67.568) Rothera Station ENVELOPE(-68.120,-68.120,-67.569,-67.569) Space Weather 20 2 |
institution |
Open Polar |
collection |
Natural Environment Research Council: NERC Open Research Archive |
op_collection_id |
ftnerc |
language |
English |
description |
It is challenging, yet important, to measure the - ever-changing - cold electron density in the plasmasphere. The cold electron density inside and outside of the plasmapause is a key parameter for radiation belt dynamics. One indirect measurement is through finding the velocity dispersion relation exhibited by lightning induced whistlers. The main difficulty of the method comes from low signal-to-noise ratios for most of the ground-based whistler components. To provide accurate electron density and L-shell measurements, whistler components need to be detectable in the noisy background, and their characteristics need to be reliably determined. For this reason precise segmentation is needed on a spectrogram image. Here we present a fully automated way to perform such an image segmentation by leveraging the power of convolutional neural networks, a state-of-the-art method for computer vision tasks. Testing the proposed method against a manually, and semi-manually segmented whistler dataset achieved <10% relative electron density prediction error for 80% of the segmented whistler traces, while for the L-shell, the relative error is <5% for 90% of the cases. By segmenting more than 1 million additional real whistler traces from Rothera station Antarctica, logged over 9 years, seasonal changes in the average electron density were found. The variations match previously published findings, and confirm the capabilities of the image segmentation technique. |
format |
Article in Journal/Newspaper |
author |
Pataki, Bálint Ármin Lichtenberger, János Clilverd, Mark Máthé, Gergely Steinbach, Péter Pásztor, Szilárd Murár‐Juhász, Lilla Koronczay, Dávid Ferencz, Orsolya Csabai, István |
spellingShingle |
Pataki, Bálint Ármin Lichtenberger, János Clilverd, Mark Máthé, Gergely Steinbach, Péter Pásztor, Szilárd Murár‐Juhász, Lilla Koronczay, Dávid Ferencz, Orsolya Csabai, István Monitoring space weather: using automated, accurate neural network based whistler segmentation for whistler inversion |
author_facet |
Pataki, Bálint Ármin Lichtenberger, János Clilverd, Mark Máthé, Gergely Steinbach, Péter Pásztor, Szilárd Murár‐Juhász, Lilla Koronczay, Dávid Ferencz, Orsolya Csabai, István |
author_sort |
Pataki, Bálint Ármin |
title |
Monitoring space weather: using automated, accurate neural network based whistler segmentation for whistler inversion |
title_short |
Monitoring space weather: using automated, accurate neural network based whistler segmentation for whistler inversion |
title_full |
Monitoring space weather: using automated, accurate neural network based whistler segmentation for whistler inversion |
title_fullStr |
Monitoring space weather: using automated, accurate neural network based whistler segmentation for whistler inversion |
title_full_unstemmed |
Monitoring space weather: using automated, accurate neural network based whistler segmentation for whistler inversion |
title_sort |
monitoring space weather: using automated, accurate neural network based whistler segmentation for whistler inversion |
publisher |
American Geophysical Union |
publishDate |
2022 |
url |
http://nora.nerc.ac.uk/id/eprint/531929/ https://nora.nerc.ac.uk/id/eprint/531929/1/Space%20Weather%20-%202022%20-%20Pataki%20-%20Monitoring%20Space%20Weather%20Using%20Automated%20Accurate%20Neural%20Network%20Based%20Whistler.pdf https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021SW002981 |
long_lat |
ENVELOPE(-68.130,-68.130,-67.568,-67.568) ENVELOPE(-68.120,-68.120,-67.569,-67.569) |
geographic |
Rothera Rothera Station |
geographic_facet |
Rothera Rothera Station |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
https://nora.nerc.ac.uk/id/eprint/531929/1/Space%20Weather%20-%202022%20-%20Pataki%20-%20Monitoring%20Space%20Weather%20Using%20Automated%20Accurate%20Neural%20Network%20Based%20Whistler.pdf Pataki, Bálint Ármin; Lichtenberger, János; Clilverd, Mark orcid:0000-0002-7388-1529 Máthé, Gergely; Steinbach, Péter; Pásztor, Szilárd; Murár‐Juhász, Lilla; Koronczay, Dávid; Ferencz, Orsolya; Csabai, István. 2022 Monitoring space weather: using automated, accurate neural network based whistler segmentation for whistler inversion. Space Weather, 20 (2), e2021SW002981. 12, pp. https://doi.org/10.1029/2021SW002981 <https://doi.org/10.1029/2021SW002981> |
op_rights |
cc_by_nc_nd_4 |
op_rightsnorm |
CC-BY-NC-ND |
op_doi |
https://doi.org/10.1029/2021SW002981 |
container_title |
Space Weather |
container_volume |
20 |
container_issue |
2 |
_version_ |
1766157760828801024 |