Effect of SC on frequency content of geomagnetic data using DWT application: SC automatic detection

Abstract

In this paper, a study is made to determine the effect of sudden commencement (SC) on the power spectrum of geomagnetic data using multiresolution analysis (MRA) of the discrete wavelet transform (DWT). The results of this study provides a guide to develop a new technique to automatically detect the SC because it could be an indicator of the onset of a geomagnetic storm. This new technique divides the original time series into different frequency sub-bands using the MRA of the DWT. Then it detects the change in a certain sub-band which shows a large change due to the SC. The geomagnetic records used in this study were 3-s resolution data collected from the Circum-Pan Pacific Magnetometer Network (CPMN). Using such high-resolution data enables us to minimize the detection error and the processing time to make a decision. The proposed algorithm is tested on one sample every three seconds of data sets collected from the CPMN. The maximum standard deviation of the algorithm detection times is observed to be fifty four seconds of the corresponding arrival times as determined by the National Geophysical Data Center (NGDC).

A comprehensive analysis of the geomagnetic storms occurred during 18 February and 2 March 2014

“…He found occasional sudden rise of the horizontal component followed by a rapid decrease lasting a few hours and a slow recovery lasting 1-3 days. These disturbances are defined as ''magnetic storms" having the various phases of the storm as (i) storm sudden commencement (SSC), (ii) initial phase, (iii) main phase and (iv) recovery phase (Araki, 1994;Ghamry, 2011, 2013;Hafez et al, 2012Hafez et al, , 2013aGhamry et al, 2013).…”

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.…”

Automated detection of geomagnetic storms with heightened risk of GIC

“…This method has been applied extensively before in varying conditions; Hafez et al (2012) used the MODWT to detect SSCs in 3-s resolution data, while Hafez et al (2013a) used the DWT on 1-s resolution data and Hafez et al (2013b) used the MODWT on 1-s resolution data. Hafez and Ghamry (2013) used MODWT on 1-min resolution data, and Ghamry et al (2013) applied the DWT to 3-s resolution data. Our method of using the MODWT as an analysis tool follows closely the method used in Hafez et al (2013b) and Hafez and Ghamry (2013).…”