Local diagnostics of aurora presence based on intelligent analysis of geomagnetic data

Andrey Vorobev; Anatoly Soloviev; Vyacheslav Pilipenko; Gulnara Vorobeva; Aliya Gainetdinova; Aleksandr Lapin; Vladimir Belakhovskiy; Alexey Roldugin

doi:10.12737/szf-92202303

UDC 53

LOCAL DIAGNOSTICS OF AURORA PRESENCE BASED ON INTELLIGENT ANALYSIS OF GEOMAGNETIC DATA Local diagnostics of aurora presence based on intelligent analysis of geomagnetic data

АА Аа

Published in Solnechno-Zemnaya Fizika · Volume 9, Issue 2 · Pages 26–34 · Rubric: Results of current research

DOI: https://doi.org/10.12737/szf-92202303

Received: 04.10.2022 Accepted: 10.04.2023 Published: 29.06.2023

Authors

Andrey Vorobev

Anatoly Soloviev

ORCID Scopus SPIN

Vyacheslav Pilipenko

ORCID ResearcherID SPIN

Gulnara Vorobeva

et al.

Aliya Gainetdinova

Aleksandr Lapin

Vladimir Belakhovskiy

ORCID

Alexey Roldugin

Andrey Vorobev

Geophysical Center RAS Russian Federation · Moscow

Ufa University of Science and Technology Russian Federation · Ufa

Anatoly Soloviev

Academic degree: doctor of physical and mathematical sciences

Science rang: professor author.academy_titles.32 ; corresponding member author.academy_titles.32

Geophysical Center RAS Director · Russian Federation · Moscow

Schmidt Institute of Physics of the Earth, RAS Russian Federation · Moscow

ORCID Scopus SPIN

Vyacheslav Pilipenko

Academic degree: doctor of physical and mathematical sciences

Schmidt Institute of Physics of the Earth RAS Russian Federation · Moscow

Geophysical Center RAS Russian Federation · Moscow

Space Research Institute Russian Federation · Moscow

ORCID ResearcherID SPIN

Gulnara Vorobeva

Ufa University of Science and Technology Russian Federation · Ufa

Aliya Gainetdinova

Academic degree: candidate of physical and mathematical sciences

Ufa University of Science and Technology Russian Federation · Ufa

Aleksandr Lapin

Ufa University of Science and Technology Russian Federation · Ufa

Vladimir Belakhovskiy

Academic degree: candidate of physical and mathematical sciences

Polar Geophysical Institute senior reseacher · Laboratory of Arctic atmosphere · Russian Federation · Apatity

Institute of Solar-Terrestrial Physics SB RAS Russian Federation

ORCID

Alexey Roldugin

Polyarnyy geofizicheskiy institut Russian Federation · Апатиты

ABSTRACT

Despite the existing variety of approaches to monitoring space weather and geophysical parameters in the auroral oval region, the issue of effective prediction and diagnostics of auroras as a special state of the upper ionosphere at high latitudes remains virtually unresolved. In this paper, we explore the possibility of local diagnostics of auroras through mining of geomagnetic data from ground-based sources. We assess the significance of indicative variables and their statistical relationship. So, for example, the application of Bayesian inference to the data from the Lovozero geophysical station for 2012–2020 has shown that the dependence of a posteriori probability of observing auroras in the optical range on the state of geomagnetic parameters is logarithmic, and the degree of its significance is inversely proportional to the discrepancy between empirical data and approximating function. The accuracy of the approach to diagnostics of aurora presence based on the random forest method is at least 86 % when using several local predictors and ~80 % when using several global geomagnetic activity indices characterizing the geomagnetic field disturbance in the auroral zone. In conclusion, we discuss promising ways to improve the quality metrics of diagnostic models and their scope.

KEYWORDS

auroras, geomagnetic variations, geomagnetic data, ascaplots, machine learning, data mining, Bayesian inference, random forest

Funding

The study was financially supported by RSF (Project No. 21-77-30010)

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Editor-in-Chief

Geliy Zherebtsov

solater@iszf.irk.ru

Deputy Editor-in-Chief

Aleksandr Stepanov

stepanov@gaoran.ru

Deputy Editor-in-Chief

Alexander Potapov

potapov@iszf.irk.ru

Executive Secretary of Editorial Board

Natalia Polyushkina

npol@iszf.irk.ru

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GOST

Vorobev A. V., Soloviev A. A., Pilipenko V. A., Vorobeva G. R., Gainetdinova A. A., Lapin A. N., Belakhovskiy V. B., Roldugin A. V. Local diagnostics of aurora presence based on intelligent analysis of geomagnetic data // Solnechno-Zemnaya Fizika. 2022. no. 2. pp. 26-34. DOI: https://doi.org/10.12737/szf-92202303. (Date of access 25.09.2026).

MLA

Andrey Vorobev, Soloviev A. A., Pilipenko V. A., Vorobeva G. R., Gainetdinova A. A., Lapin A. N., Belakhovskiy V. B., Roldugin A. V.. "Local diagnostics of aurora presence based on intelligent analysis of geomagnetic data." Solnechno-Zemnaya Fizika 9.2 (2022): 26-34. DOI: https://doi.org/10.12737/szf-92202303.

APA

Vorobev A. V., Soloviev A. A., Pilipenko V. A., Vorobeva G. R., Gainetdinova A. A., Lapin A. N., Belakhovskiy V. B., Roldugin A. V. (2022). Local diagnostics of aurora presence based on intelligent analysis of geomagnetic data. Solnechno-Zemnaya Fizika, 9(2), 26-34. https://doi.org/10.12737/szf-92202303

Chicago

Andrey Vorobev, Soloviev A. A., Pilipenko V. A., Vorobeva G. R., Gainetdinova A. A., Lapin A. N., Belakhovskiy V. B., Roldugin A. V.. "Local diagnostics of aurora presence based on intelligent analysis of geomagnetic data." Solnechno-Zemnaya Fizika 9, no. 2 (2022): 26-34. https://doi.org/10.12737/szf-92202303.

WIKI

{{cite journal | author = Vorobev A. V., Soloviev A. A., Pilipenko V. A., Vorobeva G. R., Gainetdinova A. A., Lapin A. N., Belakhovskiy V. B., Roldugin A. V. | title = Local diagnostics of aurora presence based on intelligent analysis of geomagnetic data | journal = Solnechno-Zemnaya Fizika | year = 2022 | volume = 9 | issue = 2 | pages = 26-34 | doi = 10.12737/szf-92202303 }}

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LOCAL DIAGNOSTICS OF AURORA PRESENCE BASED ON INTELLIGENT ANALYSIS OF GEOMAGNETIC DATA Local diagnostics of aurora presence based on intelligent analysis of geomagnetic data

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