Automatic Detection of Geomagnetic Sudden Commencement Using Lifting Wavelet Filters

Takano, Shigeru; Minamoto, Teruya; Arimura, Hiroki; Niijima, Koichi; Iwamoto, Toshihiko; Araki, Tohru

doi:10.1007/3-540-46846-3_22

Cited by 19 publications

(18 citation statements)

References 4 publications

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“…Since then, the working procedure to detect SSC and Sfe events has been more or less the same and it is still based on manual methods. Recently, many attempts to automatize the detection of sudden commencements (SC) have succeeded using different approaches, e.g., neural networks (Segarra and Curto, 2013), wavelet filters (Takano et al, 1999) and time-frequency clusters (Hafez and Ghamry, 2011). The key factor in the successful automatic detection of SSC was probably the fact that they mostly have a common morphological pattern: the main signal, a sudden increase in the H component, is similar around the world.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of Sfe: a proposal

et al. 2017

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Abstract. Solar flare effects (Sfe's) are rapid magnetic variations related to the enhancement of the amount of radiation produced during solar flare events. For several minutes, the ionosphere is activated and electron densities, electrical conductivities and electric currents are enhanced. The magnetic signature of a flare is visible in the illuminated hemisphere, and sometimes it shows up as small crochet-like movements in the magnetograms. However, regarding their detection using an automatized procedure, they have been very elusive because their small amplitude, which is close to the level of noise produced by other natural phenomena, and their highly variable irregular shapes make it extremely difficult to apply predesigned patterns. In this paper, we summarize the difficulties that Sfe detection presents and explain a line of work we initiated to overcome these difficulties with the goal of achieving a system capable of performing automatic detection. Some properties of Sfe's, including spherical symmetry around the vortex and different time durations between Sfe's and other natural variations, were used to construct an index allowing us to detect Sfe's.

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Section: Introductionmentioning

confidence: 99%

Automatic detection of Sfe: a proposal

et al. 2017

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“…This problem has been tackled many times before (Hafez and Ghamry 2011;Joselyn 1985;Mendes et al 2005;Shinohara et al 2005;Takano et al 1999), most recently and extensively by Hafez et al (2013a), where methods were developed for automated SSC detection in real-time geomagnetic observatory data with very promising results. These methods naturally have their limitations; the earlier studies used 1-min data, which do not have high enough resolution for very detailed or rapid detection.…”

Section: Introductionmentioning

confidence: 99%

Automated detection of geomagnetic storms with heightened risk of GIC

Bailey

Leonhardt

2016

Earth Planet Sp

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Automated detection of geomagnetic storms is of growing importance to operators of technical infrastructure (e.g., power grids, satellites), which is susceptible to damage caused by the consequences of geomagnetic storms. In this study, we compare three methods for automated geomagnetic storm detection: a method analyzing the first derivative of the geomagnetic variations, another looking at the Akaike information criterion, and a third using multi-resolution analysis of the maximal overlap discrete wavelet transform of the variations. These detection methods are used in combination with an algorithm for the detection of coronal mass ejection shock fronts in ACE solar wind data prior to the storm arrival on Earth as an additional constraint for possible storm detection. The maximal overlap discrete wavelet transform is found to be the most accurate of the detection methods. The final storm detection software, implementing analysis of both satellite solar wind and geomagnetic ground data, detects 14 of 15 more powerful geomagnetic storms over a period of 2 years.

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“…There are some authors interested in an automatic detection of SC (for example, Takano et al, 1999;Shinohara et al, 2005;or Hafez and Ghamry, 2011). Generally, they excluded from their data set the events with small amplitude and they focused their detection system in the amplitude and onset time.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of sudden commencements using neural networks

Segarra

Curto

2013

Earth Planet Sp

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The aim of this work is to develop an automatic system to detect sudden commencements (SCs). SCs are produced by a sudden increase of solar wind dynamic pressure and are detected simultaneously everywhere on the ground (Araki, 1994). Since 1975, Ebro Observatory is responsible to elaborate the list of SC, following the morphological rules given by Mayaud (1973). Nowadays, this task is still done manually and presents some difficulties; the most worrying one is the decreasing number of observatories who collaborate with this task because most of them opted for the installation of unattended observatories. Hence, the necessity of an alternative method to continue the service becomes a urgency. The automatic method presented in this work is based on neural network analysis. To succeed with neural networks, we did a previous work of characterization and parameterization of SCs by statistical analysis. In this way, we focused on the determination of the appropriate parameters to be used as the inputs of the network which resulted to be: slope, change of magnetic activity and difference of the levels before and after the jump. We worked with X component and also with Y component. An important criteria introduced in this work is the necessary coherence of the results obtained with this new automatic method with those obtained with the manual method and reported in the old list of SC. Finally, the neural network is able to recognize the SC pattern successfully, but now this is achieved in a non-manned way. A robust quasi-real-time detection can be undertaken in the future.

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Automatic Detection of Geomagnetic Sudden Commencement Using Lifting Wavelet Filters

Cited by 19 publications

References 4 publications

Automatic detection of Sfe: a proposal

Automatic detection of Sfe: a proposal

Automated detection of geomagnetic storms with heightened risk of GIC

Automatic detection of sudden commencements using neural networks

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