We suggest a wavelet-based multiscale mathematical model of geomagnetic field variations. The model is particularly capable of reflecting the characteristic variation and local perturbations in the geomagnetic field during the periods of increased geomagnetic activity. Based on the model, we have designed numerical algorithms to identify the characteristic variation component as well as other components that represent different geomagnetic field activity. The substantial advantage of the designed algorithms is their fully automatic performance without any manual control. The algorithms are also suited for estimating and monitoring the activity level of the geomagnetic field at different magnetic observatories without any specific adjustment to their particular locations. The suggested approach has high temporal resolution reaching 1 min. This allows us to study the dynamics and spatiotemporal distribution of geomagnetic perturbations using data from ground-based observatories. Moreover, the suggested approach is particularly capable of discovering weak perturbations in the geomagnetic field, likely linked to the nonstationary impact of the solar wind plasma on the magnetosphere. The algorithms have been validated using the experimental data collected at the IKIR FEB RAS observatory network.
Mandrikova, O. V., Solovyev, I. S., Khomutov, S. Y., Geppener, V. V., Klionskiy, D. M., & Bogachev, M. I. (2018). Multiscale variation model and activity level estimation algorithm of the Earth’s magnetic field based on wavelet packets. Annales Geophysicae, 36(5), 1207–1215. https://doi.org/10.5194/angeo-36-1207-2018