Using neural networks to study the geomagnetic field evolution

Abstract

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.

Keywords

• Geomagnetic Field
• Geomagnetic Observatory
• Neural Networks (NN)
• time series
• time prediction