z-logo
open-access-imgOpen Access
Probability tails of wavelet coefficients of magnetometer records
Author(s) -
Kokoszka P.,
Maslova I.,
Sojka J.,
Zhu L.
Publication year - 2006
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2005ja011486
Subject(s) - wavelet , magnetometer , wavelet transform , physics , statistical physics , probability distribution , geomagnetic storm , statistical parameter , geophysics , computational physics , computer science , mathematics , statistics , earth's magnetic field , magnetic field , artificial intelligence , quantum mechanics
The ground‐based magnetometer network has long been a powerful tool for monitoring and observing the variations of the currents flowing in the magnetosphere‐ionosphere (M‐I) system. The time series of magnetograms are nonstationary and their frequency behavior changes over time. They are therefore not amenable to traditional time domain or spectral (Fourier) analysis. In recent years, various new mathematical techniques have been developed to analyze magnetometer data and the wavelet technique has stood out as being particularly relevant. In order to correctly make statistical inferences based on wavelet analysis, the wavelet coefficient distributions of magnetograms must be examined. In this work, we apply the discrete wavelet transform to the 1‐min magnetometer records and then use several statistical techniques to analyze the probability distributions of the wavelet coefficients. It is found that the distributions of these coefficients for both storm and quiet times are highly nonnormal and can be classified as being heavy tailed. This finding suggests that when applying statistical techniques to the wavelet coefficients of the magnetograms, one must make sure that these techniques are robust to large departures from Gaussianity manifested by the presence of heavy probability tails. It is also found that the tail indexes for storm times are on average smaller than those of quiet times, which reflects the stronger impulsive and nonstationary features in magnetometer data during storm times, and the shifts are most significant for the wavelet coefficients corresponding to physical scales of 4–8 min.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here