Abstract
The automatic detection of the onset time of the geomagnetic storm sudden commencement (SSC) is of great importance for many applications. The distribution of the power along the frequency axis during the SSC was investigated. This analysis guide us to build an SSC automatic detector, for the first time, of one sample per second data based on the un-decimated discrete wavelet transform (DWT), unlike previous studies that focused on determining the SSC times using one-minute resolution data. Using such high-resolution data enabled us to achieve a small detection error and short processing time. One hundred thirty four 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 35 and 44. s, respectively, of the corresponding manually determined onset times. The proposed algorithm tested by using continuous period data (six months). The results show the capability of the algorithm to detect the SSCs successfully with low rate of false detections.
| Original language | English |
|---|---|
| Pages (from-to) | 143-152 |
| Number of pages | 10 |
| Journal | Computers and Geosciences |
| Volume | 51 |
| DOIs | |
| Publication status | Published - Feb 1 2013 |
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All Science Journal Classification (ASJC) codes
- Information Systems
- Computers in Earth Sciences
Cite this
Un-decimated discrete wavelet transform based algorithm for extraction of geomagnetic storm sudden commencement onset of high resolution records. / Hafez, Ali G.; Ghamry, Essam; Yayama, Hideki; Yumoto, Kiyohumi.
In: Computers and Geosciences, Vol. 51, 01.02.2013, p. 143-152.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Un-decimated discrete wavelet transform based algorithm for extraction of geomagnetic storm sudden commencement onset of high resolution records
AU - Hafez, Ali G.
AU - Ghamry, Essam
AU - Yayama, Hideki
AU - Yumoto, Kiyohumi
PY - 2013/2/1
Y1 - 2013/2/1
N2 - The automatic detection of the onset time of the geomagnetic storm sudden commencement (SSC) is of great importance for many applications. The distribution of the power along the frequency axis during the SSC was investigated. This analysis guide us to build an SSC automatic detector, for the first time, of one sample per second data based on the un-decimated discrete wavelet transform (DWT), unlike previous studies that focused on determining the SSC times using one-minute resolution data. Using such high-resolution data enabled us to achieve a small detection error and short processing time. One hundred thirty four 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 35 and 44. s, respectively, of the corresponding manually determined onset times. The proposed algorithm tested by using continuous period data (six months). The results show the capability of the algorithm to detect the SSCs successfully with low rate of false detections.
AB - The automatic detection of the onset time of the geomagnetic storm sudden commencement (SSC) is of great importance for many applications. The distribution of the power along the frequency axis during the SSC was investigated. This analysis guide us to build an SSC automatic detector, for the first time, of one sample per second data based on the un-decimated discrete wavelet transform (DWT), unlike previous studies that focused on determining the SSC times using one-minute resolution data. Using such high-resolution data enabled us to achieve a small detection error and short processing time. One hundred thirty four 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 35 and 44. s, respectively, of the corresponding manually determined onset times. The proposed algorithm tested by using continuous period data (six months). The results show the capability of the algorithm to detect the SSCs successfully with low rate of false detections.
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U2 - 10.1016/j.cageo.2012.07.008
DO - 10.1016/j.cageo.2012.07.008
M3 - Article
AN - SCOPUS:84870170766
VL - 51
SP - 143
EP - 152
JO - Computers and Geosciences
JF - Computers and Geosciences
SN - 0098-3004
ER -