Wavelet Analysis of Big Data in the Global Investigation of Magnetic Field Variations in Solar-Terrestrial Physics
For researchers in solar-terrestrial physics, this work provides a multi-source data analysis approach, but it is an incremental application of existing wavelet methods to a new domain.
This study applies wavelet analysis to high-frequency time series data from multiple sources (interplanetary magnetic field, ionospheric TEC, and ground geomagnetic data) to explain and predict geomagnetic phenomena, contributing to the emerging field of AstroGeoInformatics.
We provide a Wavelet analysis of Big Data in Solar Terrestrial Physics. In order to explain and predict the dynamics of the geomagnetic phenomena we analyze high frequency time series data from different sources: 1. The Interplanetary Magnetic Field (from the ACE satellite). 2. The Ionospheric parameters - TEC (from ionospheric sounding stations). 3. The ground Geomagnetic data (from ground geomagnetic observatories, located in middle geographic latitudes). We seek for correlations in the wavelet coefficients which explain the dynamics of different magnetic phenomena in the Solar Terrestrial Physics. The large variety of data used in our research from both Solar Astronomy and Earth Observations makes it a contribution to the newly developing area of AstroGeoInformatics.