A wavelet spectral analysis technique for automatic detection of geomagnetic sudden commencements

Ali G. Hafez, Essam Ghamry, Hideki Yayama, Kiyohumi Yumoto

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Maximal overlap discrete wavelet transform is used to perform spectral analysis of geomagnetic storm sudden commencements (SCs) (SSCs). This spectral analysis guided us in the development of an automatic SSC detection algorithm. The SC can be an indicator of the onset of a geomagnetic storm; in this case, it is called an SSC. The geomagnetic records used in this study were 3-s resolution data collected from the Circum-Pan Pacific Magnetometer Network. Using such high-resolution data enabled us to achieve a small detection error and short processing time. In addition to these technical merits, we introduce a new algorithm that automatically detects, for the first time, the SC from high-resolution data (sampled at the rate of 1 sample/3 s), unlike previous studies that focused on determining the SSC times automatically using 1-min data. Ninety-three geomagnetic storms were considered for testing the proposed algorithm; it was found that the average and maximum standard deviation of the errors in the detection times determined by the algorithm were 7 and 18 samples, respectively, of the corresponding manually determined arrival times.

Original languageEnglish
Article number6197227
Pages (from-to)4503-4512
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number11 PART1
Publication statusPublished - 2012

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)


Dive into the research topics of 'A wavelet spectral analysis technique for automatic detection of geomagnetic sudden commencements'. Together they form a unique fingerprint.

Cite this