Automatic fuzzy-logic recognition of anomalous activity on long geophysical records: Application to electric signals associated with the volcanic activity of La Fournaise volcano (Réunion Island)

Zlotnicki, J.; Mouël, Jean‐Louis Le; Гвишиани, А. Д.; Agayan, S. M.; Михайлов, В. О.; Богоутдинов, Ш. Р.; Kanwar, Rahul; Yvetot, P.

doi:10.1016/j.epsl.2005.01.040

Cited by 33 publications

(21 citation statements)

References 26 publications

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“…Intervals of the magnetic satation records. of magnetograms) can be found in the following papers: , [Bogoutdinov et al, 2010], [Gvishiani et al, 2008a;2008b;2010;2013a;2013b;, [Mikhailov et al, 2003], [Sidorov et al, 2012], [Soloviev et al, 2012a;2012b;, [Zelinskiy et al, 2014], [Zlotnicki et al, 2005].…”

Section: Resultsmentioning

confidence: 99%

Short introduction into DMA

Agayan¹,

Богоутдинов²,

Krasnoperov³

2018

Russ. J. Earth Sci.

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Section: Resultsmentioning

confidence: 99%

Short introduction into DMA

Agayan¹,

Богоутдинов²,

Krasnoperov³

2018

Russ. J. Earth Sci.

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“…Correspondingly, 3, 8 and 5 anomalous areas were identified, respectively, whose configuration and location are different from the anomalous areas identified by the simple measures of activity. This proves the disjunctive, conjunctive and compromise character of the complex measures of activity (7-9) (Zlotnicki et al 2005). …”

Section: Measure and Monitoring Of Activity (Examples)mentioning

confidence: 99%

“…In DMA, for a mesmax measure there are four constructions: "fuzzy comparisons", "Kolmogorov averages", "fuzzy means", "iteration bounds" (Gvishiani et al 2004;Zlotnicki et al 2005;). …”

Section: Measure Of Maximality Mesmax (Constructions)mentioning

confidence: 99%

The Study of Time Series Using the DMA Methods and Geophysical Applications

Agayan

Богоутдинов

Soloviev

et al. 2016

Data Science Journal

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“…The second dataset consists of electromagnetic horizontal measurements that were acquired on the La Fournaise volcano before, during and after the 1998 eruption (Zlotnicki et al 2005). The data were acquired with a sampling frequency of 0.05 Hz with induction coils similar to those made by Metronix or Phoenix Geophysics (Clerc 1971), and Pb-PbCl 2 electrodes.…”

Section: Datasetsmentioning

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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Automatic fuzzy-logic recognition of anomalous activity on long geophysical records: Application to electric signals associated with the volcanic activity of La Fournaise volcano (Réunion Island)

Cited by 33 publications

References 26 publications

Short introduction into DMA

Short introduction into DMA

The Study of Time Series Using the DMA Methods and Geophysical Applications

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

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