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Characterization of ionospheric total electron content data using wavelet-based multifractal formalism

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  • Bhardwaj, Shivam
  • Chandrasekhar, E.
  • Seemala, Gopi K.
  • Gadre, Vikram M.

Abstract

Understanding of the spatio-temporal behaviour of nonlinear geophysical signals, such as ionospheric total electron content (TEC) by multifractal analysis brings out the chaotic and intermittent nature of the signal under consideration. Wavelet-based multifractal analysis was performed on TEC data and the horizontal component of the Earth’s magnetic field (henceforth referred to as H-component) data recorded during geomagnetic storm events at a few sites in equatorial, mid-latitude and high latitude regions (30oS to 80oN), confined to a narrow longitude band (35oW−80oW) (geographic coordinates) during the solar minimum (2008) and solar maximum (2014) years. The study was done using the magnitude cumulant analysis of the wavelet transform. The advantage of this technique, over the well known wavelet transform modulus maxima (WTMM) method in studying the multifractal behaviour of nonlinear signals is explained. Results show that during the major geomagnetic storm events (Dst. Index ≤−50 nT) both TEC and the H-component data exhibit strong multifractal behavior and that the degree of multifractality (representative of the width of the multifractal spectrum) for the H-component data is more than that of TEC for all latitudes regardless of solar conditions. A nonlinear P-model, representative of multiplicative cascades for the above data sets, also supports the above observation. It has been observed that these observations hold good when multifractal behaviour of TEC data, with and without its dominant diurnal component, is compared with that of H-component data. A statistical hypothesis testing of the above results obtained using bootstrapping technique also establishes the significance level of the multifractal behaviour of the data.

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  • Bhardwaj, Shivam & Chandrasekhar, E. & Seemala, Gopi K. & Gadre, Vikram M., 2020. "Characterization of ionospheric total electron content data using wavelet-based multifractal formalism," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
  • Handle: RePEc:eee:chsofr:v:134:y:2020:i:c:s0960077920300527
    DOI: 10.1016/j.chaos.2020.109653
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    References listed on IDEAS

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    Cited by:

    1. Bhardwaj, Shivam & Gadre, Vikram M. & Chandrasekhar, E., 2020. "Statistical analysis of DWT coefficients of fGn processes using ARFIMA(p,d,q) models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    2. Guan, Sihai & Wan, Dongyu & Yang, Yanmiao & Biswal, Bharat, 2022. "Sources of multifractality of the brain rs-fMRI signal," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

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