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\begin{document}

\title{Artificial deep neural network modeling of solar- and atmospheric-driven
ground magnetic perturbations at mid-latitude}



\author[1]{Rungployphan Kieokaew}%
\author[2]{Veronika Haberle}%
\author[2]{Aurelie Marchaudon}%
\author[1]{Pierre-Louis Blelly}%
\author[3]{Aude Chambodut}%
\affil[1]{Institut de Recherche en Astrophysique et Planétologie}%
\affil[2]{Institut de Recherche en Astrophysique et Planétologie, CNRS, CNES et Université Paul Sabatier}%
\affil[3]{Université de Strasbourg, CNRS, ITES UMR7063}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{abstract}
Ground magnetic observatories measure the Earth's magnetic field and its
coupling with the solar wind responsible for ionospheric and
magnetospheric current systems. Predicting effects of solar- and
atmospheric-driven disturbances is a crucial task. Using data from the
magnetic observatory Chambon-la-For\selectlanguage{ngerman}Ãªt at mid-latitude, we investigate
the capability of our developed deep artificial neural networks in the
modeling of the contributions above 24 hours and the daily variations.
Two neural networks were built with the long short-term memory
architecture with multiple layers. Using the data from 1995 onwards, the
neural networks were trained with physical parameters indicative of
solar variabilities and geographical daily and seasonal variations. By
excluding the secular variation owing to the change of the Earth's
intrinsic magnetic field, we demonstrate that our approach can model the
observed signals with overall good agreements for both a solar-quiet
period in 2009 and a solar-active period in 2012. Particularly, using
the walk forward training, we updated our models with new data leading
up to the test year. The implication of this work is twofold. First, our
approach can be adapted for near real-time prediction of intensity of
solar and atmospheric disturbances. Second, the neural networks can be
used to model the quiet variations when excluding the solar
variabilities with important applications in the calculation of magnetic
activity indices. This work is a proof-of-concept that deep neural
networks can be used to model solar- and atmospheric-driven
perturbations modulated by daily and seasonal variations as recorded at
a ground magnetic observatory.%
\end{abstract}\selectlanguage{ngerman}%



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