Solar-Terrestrial Physics Solar-Terrestrial Physics Solar-Terrestrial Physics 2500-0535 53266 10.12737/stp-92202303 Results of current research Results of current research Results of current research Local diagnostics of aurora presence based on intelligent analysis of geomagnetic data Local diagnostics of aurora presence based on intelligent analysis of geomagnetic data Воробьев Андрей Владимирович Vorobev Andrey Vladimirovich geomagnet@list.ru https://orcid.org/0000-0002-6476-9471 Соловьёв Анатолий Александрович Soloviev Anatoly Aleksandrovich rjes@wdcb.ru

доктор физико-математических наук;

doctor of physical and mathematical sciences;

 https://orcid.org/0000-0003-3056-7465 Пилипенко Вячеслав Анатольевич Pilipenko Vyacheslav Anatolievich space.soliton@gmail.com

доктор физико-математических наук;

doctor of physical and mathematical sciences;

 Воробьева Гульнара Равильевна Vorobeva Gulnara Ravilevna gulnara.vorobeva@gmail.com Гайнетдинова Алия Айдаровна Gainetdinova Aliya Aydarovna gajnetdinova.aa@ugatu.su

кандидат физико-математических наук;

candidate of physical and mathematical sciences;

 Лапин Александр Николаевич Lapin Aleksandr Nikolaevich meccos160@yandex.ru Белаховский Владимир Борисович Belahovskiy Vladimir Borisovich belakhov@mail.ru

кандидат физико-математических наук;

candidate of physical and mathematical sciences;

 Ролдугин Алексей Валентинович Roldugin Alexey Valentinovich Геофизический центр РАН Москва Россия Geophysical Center RAS Moscow Russian Federation Уфимский университет науки и технологий Уфа Россия Ufa University of Science and Technology Ufa Russian Federation Геофизический центр РАН Москва Россия Geophysical Center RAS Moscow Russian Federation Институт физики Земли им. О.Ю. Шмидта РАН Москва Россия Schmidt Institute of Physics of the Earth, RAS Moscow Russian Federation Институт физики Земли им. О.Ю. Шмидта РАН Москва Россия Schmidt Institute of Physics of the Earth RAS Moscow Russian Federation Геофизический центр РАН Москва Россия Geophysical Center RAS Moscow Russian Federation Институт космических исследований Москва Россия Space Research Institute Moscow Russian Federation Уфимский университет науки и технологий Уфа Россия Ufa University of Science and Technology Ufa Russian Federation Уфимский университет науки и технологий Уфа Россия Ufa University of Science and Technology Ufa Russian Federation Уфимский университет науки и технологий Уфа Россия Ufa University of Science and Technology Ufa Russian Federation Полярный геофизический институт Апатиты Россия Polar Geophysical Institute Apatity Russian Federation Полярный геофизический институт Апатиты Россия Полярный геофизический институт Апатиты Russian Federation 29 06 2023 29 06 2023 9 2 22 30 04 10 2022 10 04 2023 https://zh-szf.ru/en/nauka/article/53266/view

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.

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.

 auroras geomagnetic variations geomagnetic data ascaplots machine learning data mining Bayesian inference random forest auroras geomagnetic variations geomagnetic data ascaplots machine learning data mining Bayesian inference random forest The study was financially supported by RSF (Project No. 21-77-30010) The study was financially supported by RSF (Project No. 21-77-30010)

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