- Open Access
Effect of SC on frequency content of geomagnetic data using DWT application: SC automatic detection
© The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS); The Seismological Society of Japan; The Volcanological Society of Japan; The Geodetic Society of Japan; The Japanese Society for Planetary Sciences; TERRAPUB. 2013
Received: 14 July 2011
Accepted: 30 April 2013
Published: 9 October 2013
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).
It is known that a geomagnetic storm is one of the most prominent phenomena in the geospace environment. Moos (1910) identified a well-defined pattern in the so called “X disturbance” in the horizontal component (H-component) at Colaba, India. He found an occasional sudden rise of the horizontal component followed by a rapid decrease lasting a few hours and a slow recovery lasting 2–3 days. These disturbances are defined as “magnetic storms” and the various phases of the storm have been named by Chapman (1918) and Araki (1994) as: (i) sudden storm commencement (SC); (ii) initial phase; (iii) main phase; and (iv) the recovery phase.
The Dst index is commonly used in studies of magnetic storms as an indicator of the intensity of the ring current or magnetic storms. It is known that the SC is not a necessary condition for a storm to occur, and hence the initial phase is not an essential feature. But the essential feature of a storm is the significant development of a ring current.
The term Sudden Impulse (SI) is used for a sudden increase in the H-component without an ensuing magnetic storm. SIs were first regarded as magnetic disturbances with a different nature from SCs. However, SIs have been proved to be caused by the same physical mechanisms as SCs and should be classified as being complementary to SCs according to Curto et al. (2007).
The automatic detection of sudden commencements which are followed by magnetic storms is of great importance, as these events often affect radio and television interference and blackouts, which are hazards to orbiting astronauts and spacecrafts and power grids.
Joselyn (1985) has pointed out the need for an automatic detection of SC in real time to alert forecasters of potential geomagnetic storm conditions. Mendes et al. (2005) detected variations of the H-component of the geomagnetic field related to geomagnetic storms by means of wavelets. Shinohara et al. (2005) developed an automatic real-time detection and warning system of geomagnetic sudden commencements using real-time data from ground-based geomagnetic observations.
They developed a method based on statistical analysis to detect the SCs using the amplitude and increasing rate of the geomagnetic field. The forecasting of magnetic storms by using a moving gradient of the SYM-H index (the symmetric portion of the horizontal component of the magnetic field) as a detecting algorithm has been pointed out by Khabarova et al. (2006).
The automatic detection of the onset time of natural events is common in the literature. Hafez et al. (2009, 2010) used a spectrogram and a DWT, respectively, to detect earthquake onset arrival times. Takano et al. (1999) used lifting wavelet filters to build an SC detection algorithm for one-minute data resolution recorded at the KAG station. Hafez and Ghamry (2011) used time-frequency clusters to automatically detect SC times applied on one-minute data resolution and compared their algorithm with that of Takano et al. (1999). In this paper, we introduce a new algorithm to automatically detect SC times for three-second resolution data. The algorithm proposed by Hafez and Ghamry (2011) cannot be used for the three-second resolution data as the frequency content is totally different from the frequency content of the one-minute data.
Motivated by these facts, an algorithm for the automatic detection of geomagnetic sudden commencements is introduced using the details of multiresolution analysis (MRA) of a discrete wavelet transform (DWT). In the proposed algorithm, discrete wavelet coefficients of segments containing SCs are calculated at different scales. Using these coefficients, detailed features at each scale are determined. Among these details, we choose the second-stage detail in detecting SC because in this detail the arrival is very clear which enables us to detect the SC time correctly.
2. Multiresolution Analysis (MRA)
3. Data Set
List of magnetic observatories used in our study.
4. Effect of SC on Geomagnetic Data Spectrum
In Fig. 3, there are two SCs recorded at the KAG station: the first one was recorded on April 16, 1997, while the second SC was on December 17, 1992. In the first SC we can recognize that the trace has no discontinuity starting from the arrival time of this SC to the end of its rise time. The rate of variation of this SC can be calculated using the period between the two vertical lines which are drawn in the first SC. For the second SC, a discontinuity can be recognized in the trace during the rise time. Therefore, the rate of variation for the second SC is calculated over the trace between the two vertical lines which are drawn on the second SC. The rates of variation for the first and second SCs are 0.17 and 0.62 nT/sample, respectively, where the sampling rate is one sample per three seconds.
The first window of the first column is an SC recorded at the KAG station on April 16, 1997. The arrival time of this SC in the time domain is at 13.33 UT. In the second window of the first column, we note no change in the power of the first detail around 13.33 UT. On the other hand, in the third and fourth windows of the first column, the power of the second and third details around 13.33 UT show a clear increase. The rate of variation of this SC is 0.17 nT/sample.
The SC at the top of the second column was recorded at the ONW station on May 28, 1994. The arrival timing of this SC is around 13.95 UT. Similarly to the first SC, although there is no change in the power of the first detail around 13.95 UT, a significant increase in power is observed in the second and third details around 13.95 UT. The rate of variation of this SC is 0.4 nT/sample.
In the same fashion, the third SC in the third column, which was recorded at the KAG station on May 15, 1997, the arrival time is around 01.99 UT. Although there is no change in the power of the first detail, around 01.99 UT, there is a clear increase in the power of the second and third details. The rate of variation of this SC is 0.4 nT/sample.
The SC in the fourth column was recorded at the KAG station on December 17, 1992. The arrival time is at 06.25 UT, a slight increase in power of the first detail around 06.25 UT can be recognized where as a large increase in the power of the second and third details around 06.25 UT is detected. The rate of variation of this SC is 0.62 nT/sample.
The SC in the fifth column is recorded at the ONW station on November 22, 1997. The arrival time of the SC is at 09.825 UT. A small increase in power in the first detail around 09.825 UT can be seen, and a clear increase in power in the second and third details around 09.825 UT is observed. The rate of variation of this SC is 0.92 nT/sample.
From the explanation given regarding these five SCs, we can conclude that the SCs with a low rate of variation do not result in a change in the power of the first detail, which is found in the frequency band 41.67–83 mHz. A clear change in the power of the second and third details can be detected due to the same low-rate SCs. The frequency bands of the second and third details are 20.8–41.67 mHz and 10.38–s 20.8 mHz, respectively. With the increase of the rate of variation of SCs, a slight increase in power of the first detail can be recognized as shown in the first detail of the fourth and fifth SCs of Fig. 4.
5. SC Automatic Detector
There are advantages of building an SC automatic detection algorithm using high-resolution data instead of the one-minute data adopted by Shinohara et al. (2005), Hafez and Ghamry (2011) and others. The first advantage is a reduction of the automatic detection error. Assuming that both algorithms made an error of two samples in detection, in the former study, using a one-minute sampling data, this resulted in a 120-s error, whereas in the present study, with a 3-s sampling data, it involved a 6-s error. The second advantage is a reduction of the automatic detection delay. In the algorithm proposed by Shinohara et al. (2005) the decision is given based on three parameters, which are the amplitude, the time required for the increase and maximum time variation, which means that the delay is more than the time required for an increase which ranges from 2–10 minutes. Hafez and Ghamry (2011) used a four-samples window to search for an SC, which means a four-minute delay after receiving the data from the stations. In our proposed algorithm, the window is five samples which means 15 seconds delay after receiving the data. Therefore, it is favorable to use high-resolution data to perform fast and accurate SC detection.
In Fig. 6, a-1 and a-2 show the H-component of the geomagnetic field for an entire day and the enlarged segment of this field around SC, respectively. b-1 is the averaged squared second detail of the entire day. In (b-2), the segment around the SC in the second detail is enlarged. In this subfigure, we can notice that the condition of alarming for a trigger is satisfied when the amplitudes of the second detail exceed the triggering threshold over a period larger than the triggering period.
In (b-3), a spike during which the amplitude exceeds the threshold can be seen. This spike doesn't alarm for a trigger as the duration of such a spike is less than the triggering period. There is a possibility of the occurrence of the alarming condition when there is no SC; in such a situation, this trigger is called a false trigger. This possibility, of false triggers, is low in normal days but it is observed to increase after the occurrence of an SC. In order to confirm that this trigger is an SC and not a false trigger, the other stations are checked if they alarm for a trigger within a certain period around the time of this alarm. If a trigger is found at these stations, this trigger is declared to be an SC, otherwise this trigger is declared as a false trigger. This certain period is considered as an accepted margin, and can be set by the user. In this paper, we propose three minutes for this specific period. Examples explaining this point in detail will be described in the Section 6, and are shown in Table 2.
The proposed SC detection algorithm was tested for 93 magnetic storms, recorded by the stations listed in Table 1. The SC times calculated by the present algorithm are compared with the times listed in the web-site of the National Geophysical Data Center (NGDC) of the National Oceanic and Atmospheric Administration (NOAA). It was found that unsuccessful detections were only eight SCs, which is a small number.
Arbitrarily chosen SCs from the checked data set. These examples show the capability of the algorithm to determine the SC timing correctly and its immunity against alarming false triggers. Times are expressed in UT of the respective days and in units of sample (1 sample = 3 s).
Date and station
Arrival times of SCs by the NGDC (samples)
Algorithm detection times
Rate of variation of SC
13255, 15930, 19705, 24380, 25025
13250, 13335, 13680, 14155, 14390
1925, 2000, 2150, 2525, 2640
1925, 2005, 2150, 2390, 2510
1765, 7080, 9325, 9670, 10510
9325, 9395, 9635, 9700, 10300
9365, 10585, 10630, 11815, 19155
4770, 13010, 13725, 20395, 20605
20600, 20715, 20845, 21050, 21340
5250, 20610, 20755, 21055, 21290
The effect of sudden commencement on geomagnetic data spectrum is investigated in this paper using the DWT. This effect provided the guide to build a new algorithm for determining geomagnetic storm sudden commencement onset times. It is proved that the change in the spectrum during the SC is directly proportional to the value of the SC rate of variation. Investigations elaborate that the second detail of the MRA of the DWT is the best detail to be used to monitor the SC timing. Case studies have shown that the algorithm is very sensitive to both low- and high-rate variation SCs. Applying the algorithm on a data set from the Circum-pan Pacific Magnetometer Network (CPMN), it is found that the time of SC arrivals computed by our proposed algorithm is very close to those observed by the NGDC.
The results presented in this paper rely on data collected from the Circum-pan Pacific Magnetometer Network (CPMN). The authors would like to thank the staff members of CPMN.
- Araki, T., A physical model of the geomagnetic sudden commencement, Geophysical Monograph, 81, 183–200, 1994.Google Scholar
- Chapman, S., On the times of sudden commencements of magnetic storms, Proc. Phys. Soc., 30, 205–214, 1918.Google Scholar
- Curto, J. J., T. Araki, and L. F. Alberca, Evolution of the concept of Sudden Storm Commencements and their operative identification, Earth Planets Space, 59, i–xii, 2007.View ArticleGoogle Scholar
- Hafez, A. G. and E. Ghamry, Automatic detection of geomagnetic sudden commencements via time frequency clusters, Adv. Space Res., 48, 1537–1544, 2011.View ArticleGoogle Scholar
- Hafez, A. G., T. A. Khan, and T. Kohda, Earthquake onset detection using spectro-ratio on multithreshold time-frequency sub-band, Digital Signal Process., 19(1), January 2009, 118–126, 2009.View ArticleGoogle Scholar
- Hafez, A. G., T. A. Khan, and T. Kohda, Clear P-wave arrival of weak events and automatic onset determination using wavelet filter banks, Digital Signal Process, 20(3), May 2010, 715–723, 2010.View ArticleGoogle Scholar
- Joselyn, J. A., The automatic detection of geomagnetic-storm sudden commencements, Adv. Space Res., 5(4), 193–197, 1985.View ArticleGoogle Scholar
- Khabarova, O., V. Pilipenko, M. J. Engebretson, and E. Rudenchik, Solar wind and interplanetary magnetic field features before magnetic storm onset, Proc. Of International Conference of Substorms, 8, 1–6, 2006.Google Scholar
- Mendes, O. J., M. O. Domingues, A. M. da Costa, and A. L. Clua de Gonzalez, Wavelet analysis applied to magnetograms: Singularity detections related to geomagnetic storms, J. Atmos. Sol.-Terr. Phys., 67, 1827–1836, 2005.View ArticleGoogle Scholar
- Moos, N. A. F., Magnetic observations made at the government observatory Bombay 1846–1905 and their discussion. Part II, The Phenomenon and Its Description, 1910.Google Scholar
- Percival, D. B. and A. T. Walden, Wavelet Methods for Time Series Analysis, Cambridge: Cambridge University Press, 2000.View ArticleGoogle Scholar
- Shinohara, M., T. Kikuchi, and K. Nozaki, Automatic realtime detection of sudden commencement of geomagnetic storms, J. NICT, 52(3/4), 197–205, 2005.Google Scholar
- Solar-Geophysical Data, http://sgd.ngdc.noaa.gov/, National Geophysical Data Center (NGDC), NOAA.
- Takano, S., T. Minamoto, H. Arimura, K. Niijima, T. Iyemori, and T. Araki, Automatic detection of geomagnetic sudden commencement using lifting wavelet filters, Lecture Notes in Computer Science, Springer Berlin / Heidelberg Volume 1721, 1999.Google Scholar