Using neural networks to study the geomagnetic field evolution

study their time evolution in years. In order to find the best NN for the time predictions, we tested many different
kinds of NN and different ways of their training, when the inputs and targets are long annual time series of
synthetic geomagnetic field values. The found NN was used to predict the values of the annual means of the
geomagnetic field components beyond the time registration periods of a Geomagnetic Observatory. In order to
predict a time evolution of the global field over the Earth, we considered annual means of 105 Geomagnetic
Observatories, chosen to have more than 30 years registration (1960.5-2005.5) and to be well distributed over
the Earth. Using the NN technique, we created 137 «virtual geomagnetic observatories» in the places where
real Geomagnetic Observatories are missing. Then, using NN, we predicted the time evolution of the three
components of the global geomagnetic field beyond 2005.5.

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