Automatic Characterisation of Magnetic Indices with Artificial Intelligence

Abstract: The complex interactions between the Sun and Earth are referred to as Space Weather. Key parameters include magnetic indices which quantitatively describe geomagnetic activity by determining a baseline that removes the background magnetic field and allows quantification of the remaining activity during geomagnetic events. However, most used indices have a low temporal resolution and rely on a sparse and frozen network of ground magnetic observatories. This thesis introduces a novel way of determining the baseline for future high temporal and spatial resolution magnetic indices. Firstly, the main phenomena and effects of Space Weather are outlined, followed by a review of currently used magnetic indices and their derivation. The computation of a novel baseline introduced in this work relies on basic statistical methods which are applied on magnetic data from a dense and flexible network of ground observatories for the period 1991-2016. The focus is on the investigation of geomagnetic quiet periods for which average annual activity at each observatory is determined. A global latitudinal normalisation function with dependency on solar activity for quiet periods is found. The analysis of the newly derived baseline shows that it provides the temporal, spatial and amplitudinal resolution needed to characterise geomagnetic disturbances adequately. The residual signal has the capability of being used as the basis for further quiet period studies. A first attempt of new indices based on the introduced derivation shows a good agreement with already existing high temporal and spatial resolution magnetic indices. Future indices derived with this baseline lay a favourable fundament for the application of articial intelligence methods.