Wavelet analysis applied to magnetograms: Singularity detections related to geomagnetic storms

Mendes, Odim; Domingues, Margarete Oliveira; Costa, Aracy Mendes da; González, Alicia

doi:10.1016/j.jastp.2005.07.004

Cited by 58 publications

(51 citation statements)

References 21 publications

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“…With a local decomposition of a multiscale signal, wavelet analysis is particularly suited to detect and follow the time evolution of the frequency distribution of a given time series (Ivanov et al, 2001;Kovacs et al, 2001;Lui, 2002;Mendes et al, 2005;Mandea and Balasis, 2006;Zaourar et al, 2006;Gaci et al, 2010). This property is particularly important when one considers non-stationary processes.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based multiscale analysis of geomagnetic disturbance

Zaourar¹,

Hamoudi²,

Mandéa

et al. 2013

Earth Planet Sp

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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.

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Section: Introductionmentioning

confidence: 99%

Wavelet-based multiscale analysis of geomagnetic disturbance

Zaourar¹,

Hamoudi²,

Mandéa

et al. 2013

Earth Planet Sp

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“…As observed by Mendes et al (2005) Klausner et al (2014a, 2016a for different observatories and magnetic storms, the periods of 3, 6 and 12 min were able to identify the sudden storm commencement (SSC) and the storm main phase (SMP) in all magnetic observatory wavelet signatures. These magnetic field fluctuations on the ground are associated with variations in the ring current, and it is possible to infer that the wavelet signatures in the first three levels are related to the ring current in this timescale range, which consequently is related to Pci 5 pulsations.…”

Section: Identification Of Local Magnetic Disturbancementioning

confidence: 67%

“…As an alternative to the traditional Dst index (Sugiura, 1964), new indexes were developed using DWT, such as the wavelet-based index of storm activity (WISA; Jach et al, 2006;Xu et al, 2008) and Wp (Nosé et al, 2012), among others. Motivated by the works of Mendes et al (2005), Mendes da Costa et al (2011), Hafez et al (2013) and Klausner et al (2014aKlausner et al ( , b, 2016a, which use the DWT to detect transition regions and to localize singularities in geophysical data, in this work, the geomagnetic variation associated with the events of CIRs and HSSs in 2007 will be analyzed. To accomplish this task, the DWT will be used in three levels of decomposition to calculate the effectiveness wavelet coefficients (EWCs).…”

Section: Klausner Et Al: Analysis To Identify Local Geomagneticalmentioning

confidence: 99%

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An alternative way to identify local geomagnetically quiet days: a case study using wavelet analysis

et al. 2016

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Abstract. This paper proposes a new method to evaluate geomagnetic activity based on wavelet analysis during the solar minimum activity (2007). In order to accomplish this task, a newly developed algorithm called effectiveness wavelet coefficient (EWC) was applied. Furthermore, a comparison between the 5 geomagnetically quiet days determined by the Kp-based method and by wavelet-based method was performed. This paper provides a new insight since the geomagnetic activity indexes are mostly designed to quantify the extent of disturbance rather than the quietness. The results suggest that the EWC can be used as an alternative tool to accurately detect quiet days, and consequently, it can also be used as an alternative to determine the Sq baseline to the current Kp-based 5 quietest days method. Another important aspect of this paper is that most of the quietest local wavelet candidate days occurred in an interval 2 days prior to the high-speed-stream-driven storm events. In other words, the EWC algorithm may potentially be used to detect the quietest magnetic activity that tends to occur just before the arrival of high-speed-stream-driven storms.

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“…Geomagnetic storms are also low-frequency sources of induction. They have already been studied with wavelet analysis (e.g., Mendes et al 2005). We show, for example, in Fig.…”

Section: Other Transient Applicationsmentioning

confidence: 99%

New application of wavelets in magnetotelluric data processing: reducing impedance bias

2016

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Magnetotelluric (MT) data consist of the sum of several types of natural sources including transient and quasiperiodic signals and noise sources (instrumental, anthropogenic) whose nature has to be taken into account in MT data processing. Most processing techniques are based on a Fourier transform of MT time series, and robust statistics at a fixed frequency are used to compute the MT response functions, but only a few take into account the nature of the sources. Moreover, to reduce the influence of noise in the inversion of the response functions, one often sets up another MT station called a remote station. However, even careful setup of this remote station cannot prevent its failure in some cases. Here, we propose the use of the continuous wavelet transform on magnetotelluric time series to reduce the influence of noise even for single site processing. We use two different types of wavelets, Cauchy and Morlet, according to the shape of observed geomagnetic events. We show that by using wavelet coefficients at clearly identified geomagnetic events, we are able to recover the unbiased response function obtained through robust remote processing algorithms. This makes it possible to process even single station sites and increase the confidence in data interpretation.

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Wavelet analysis applied to magnetograms: Singularity detections related to geomagnetic storms

Cited by 58 publications

References 21 publications

Wavelet-based multiscale analysis of geomagnetic disturbance

Wavelet-based multiscale analysis of geomagnetic disturbance

An alternative way to identify local geomagnetically quiet days: a case study using wavelet analysis

New application of wavelets in magnetotelluric data processing: reducing impedance bias

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