Quantifying Schumann resonances' variation over time through statistical differences

Abstract

Schumann resonances’ statistical parameters vary over time, which gives way to analyze them as a stochastic process. By splitting the Schumann resonance’s records into segments and obtaining their empirical distribution function, the differences can be evaluated by calculating the Kolmogorov–Smirnov distance between them. The analysis’ results allow for the characterization of the Schumann resonance’s variations, along with the typical values it reaches under the chosen metric. It is shown how divergence is mitigated through sample averaging, and also how divergent samples impact on the Fast Fourier Transform algorithm.

Divergence quantification adds a layer of information for data processing. Knowing the changes experienced over time in Schumann resonances gives a way to know what kind of mathematical procedures can be applied to the signal. Quantification of signal variations over time can identify error sources in specific procedures, filter out samples unfit under certain analyses, or serve as a stop criteria for cumulative analyses.