Wavelet-based multiscale analysis of geomagnetic disturbance

Abstract

The dynamics of external contributions to the geomagnetic field is investigated by applying time-frequency methods to magnetic observatory data. Fractal models and multiscale analysis enable obtaining maximum quantitative information related to the short-term dynamics of the geomagnetic field activity. The stochastic properties of the horizontal component of the transient external field are determined by searching for scaling laws in the power spectra. The spectrum fits a power law with a scaling exponent β, a typical characteristic of self-affine time-series. Local variations in the power-law exponent are investigated by applying wavelet analysis to the same time-series. These analyses highlight the self-affine properties of geomagnetic perturbations and their persistence. Moreover, they show that the main phases of sudden storm disturbances are uniquely characterized by a scaling exponent varying between 1 and 3, possibly related to the energy contained in the external field. These new findings suggest the existence of a long-range dependence, the scaling exponent being an efficient indicator of geomagnetic activity and singularity detection. These results show that by using magnetogram regularity to reflect the magnetosphere activity, a theoretical analysis of the external geomagnetic field based on local power-law exponents is possible.

Vertical coupling of atmospheres: dependence on strength of sudden stratospheric warming and solar activity

“…Figure 5 shows the amplitudes of the semidiurnal (SD; thin continuous lines), an estimate of the SD envelope (SD envelope ; thick continuous lines), and quasi-16-day (dashed) periodic variations in EEJ strength during the years of the current study. These amplitudes are obtained using the wavelet-based spectral analysis technique (Torrence and Compo 1998;Zaourar et al 2013). It is known from the wavelet theory that the amplitudes of the harmonic components, like tides and PWs, can be obtained from the absolute value of the wavelet transform of the time series with the Morlet function as mother wavelet.…”

Method for modeling of the components of ionospheric parameter time variations and detection of anomalies in the ionosphere

“…As the latest research (Huang et al 1998;Odintsov et al 2000;Rilling 2003;Huang and Wu 2008;Klionsky et al 2008Klionsky et al , 2009Hamoudi et al 2009;Kato et al 2009;Yu et al 2010;Akyilmaz et al 2011;He et al 2011;Mandrikova et al 2012aMandrikova et al , 2012bMandrikova et al , 2014aGhamry et al 2013;Zaourar et al 2013) shows, the most natural and effective way of representing such data is the construction of non-linear adaptive approximating schemes. As a result, methods of empirical mode decomposition (Huang et al 1998;Rilling 2003;Klionsky et al 2008Klionsky et al , 2009Huang and Wu 2008;Yu et al 2010) and adaptive wavelet decomposition (Hamoudi et al 2009;Kato et al 2009;Akyilmaz et al 2011;He et al 2011;Mandrikova et al 2012aMandrikova et al , 2012bMandrikova et al , 2013aMandrikova et al , 2014aGhamry et al 2013;Zaourar et al 2013) are being intensively developed at present. Given the large variety of orthogonal basis wavelets with compact support and the presence of numerically stable fast algorithms for data transformation, wavelet decomposition provides many possibilities for the analysis of data with a complex structure (Chui 1992;Daubechies 1992;Mallat 1999), including geophysical data (Hamoudi et al 2009;...…”

“…As a result, methods of empirical mode decomposition (Huang et al 1998;Rilling 2003;Klionsky et al 2008Klionsky et al , 2009Huang and Wu 2008;Yu et al 2010) and adaptive wavelet decomposition (Hamoudi et al 2009;Kato et al 2009;Akyilmaz et al 2011;He et al 2011;Mandrikova et al 2012aMandrikova et al , 2012bMandrikova et al , 2013aMandrikova et al , 2014aGhamry et al 2013;Zaourar et al 2013) are being intensively developed at present. Given the large variety of orthogonal basis wavelets with compact support and the presence of numerically stable fast algorithms for data transformation, wavelet decomposition provides many possibilities for the analysis of data with a complex structure (Chui 1992;Daubechies 1992;Mallat 1999), including geophysical data (Hamoudi et al 2009;Kato et al 2009;Akyilmaz et al 2011;He et al 2011;Mandrikova et al 2012aMandrikova et al , 2012bMandrikova et al , 2013aMandrikova et al , 2014aGhamry et al 2013;Zaourar et al 2013). In this paper, a multiscale wavelet decomposition (MSA) of an ionospheric parameter time series was used.…”

Spectral analysis of auroral geomagnetic activity during various solar cycles between 1960 and 2014

“…The Lomb-Scargle method in particular served to verify the Morlet wavelet analysis results. Wavelet transforms were also applied by Balasis et al (2006) to perform a fractal spectral analysis of the 2001 Disturbance stormtime (Dst) index time series at solar maximum, while Zaourar et al (2013) performed a wavelet-based multiscale analysis of geomagnetic disturbances recorded at various magnetic observatories. Kunagu et al (2013) also analysed 10 years of CHAMP (CHAllenging Minisatellite Payload) magnetic field data using wavelets and found external field periodicities of interest to global geomagnetic field models.…”