Mono- and multi-fractal investigation of scaling properties in temporal patterns of seismic sequences

Telesca, Luciano; Lapenna, Vincenzo; Macchiato, M.

doi:10.1016/s0960-0779(03)00188-7

Cited by 125 publications

(63 citation statements)

References 29 publications

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“…where the width of the spectrum is zero for a mono-fractal series (Shimizu et al, 2002;Telesca et al, 2004;Telesca et al, 2015). For q = 1, (ℎ) can be interpreted as a fractal dimension, while q = 2 and q = 3 represents the information dimension and the correlation dimension of a time series, respectively.…”

Section: Learning the Fractality Of Signal By Mf-dfa 10mentioning

confidence: 99%

“…The parameter returns the asymmetrical trend of 20 spectrum. In particular, a left tail spectrum represents that the dynamics of time series is controlled by the large transient fluctuations, with positive values of , while a right tail spectrum indicates the relative dominance of small smoothly varying fluctuations, with negative values of (Telesca et al, 2004). Hence, when spectrum has a long left tail shape it can be inferred that scattering is pronounced in the region, as a result, the complexity of a multifractal spectrum may provide a crude insight into the heterogeneity of medium.…”

Section: Learning the Fractality Of Signal By Mf-dfa 10mentioning

confidence: 99%

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Constraining on the Stationarity of Signal with Time-Frequency Surrogates to Enhance the Reliability of Singularity Spectrum Attributes of Random Seismic Noise Wavefield

Hamed

Shomali

Moradi

2017

Preprint

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Abstract.Existence of a self-affine long range persistence in the seismic noise time series evidences that the current state of system is not in the pure diffused regime and transition from coherent to incoherent motion is still on progress. Rate of this 15 evolving transition can be indirectly linked to the degree of heterogeneity of medium, thus in this paper we tried to gain an insight into the heterogeneity of the medium by analyzing the width, extreme and asymmetrical trend of multifractal spectrum of seismic records. Nonetheless, toward high frequency ranges a seismic signal itself loses its stationarity short while after its recording onset time. Experimentally, the long-range correlation of a stationary time series (with 0 < ℎ(2) < 1) can be discerned from a non-stationary process (with ℎ(2) > 1) by examining the values of scaling exponent ℎ(2), 20 however, changing in the fractal properties in the crossover time scales in time series don't permit us to ascribe a single amount for ℎ(2) and without executing additional analysis on the stationarity length of signals, direct calculation of such long range correlation and fractal dimensions might be biased. Hence, in this paper we examined the inherent stationarity of a signal relative to the different observation scales in the stochastic contexts before feeding the signal into the cycle of DFA.This method is based on the comparison between global and local features of the original signal and its synthesized time-25 frequency surrogates; therefor it can effectively improve the accuracy of results. Our approach proves the existence of a high-velocity anomalous feature in the right flank of Sahand inactive volcano where it is surrounded by heterogeneous lowvelocity structures and extended to the shallower than ~3 km depth beneath this region at the northwestern of Iran.

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Section: Learning the Fractality Of Signal By Mf-dfa 10mentioning

confidence: 99%

Section: Learning the Fractality Of Signal By Mf-dfa 10mentioning

confidence: 99%

Constraining on the Stationarity of Signal with Time-Frequency Surrogates to Enhance the Reliability of Singularity Spectrum Attributes of Random Seismic Noise Wavefield

Hamed

Shomali

Moradi

2017

Preprint

View full text Add to dashboard Cite

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“…For the study of scaling behavior of various nonseries, MF-DFA has been applied quite successfully in of science and engineering [64][65][66][67][68][69][70]. MF-DFA is a nonlinear analysis technique, the of which on a given set of data provides about any evidence of selfsimilarity or persistence in the series [71].…”

Section: Archivos De Medicina Issn 1698-9465 Journal Of Neurology Andmentioning

confidence: 99%

Chaos Based Quantitative Electro-Diagnostic Marker for Diagnosis of Myopathy, Neuropathy and Motor Neuron Disease

Ghosh¹,

Dutta²,

Chakraborty³

et al. 2017

J Neurol Neurosci

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Background: Myopathies (MYO) are a group of disorders in which the muscle fibers do not function for any one of many reasons, resulting in muscular weakness and/ or muscle dysfunction. Neuropathies (NEURO) describe damage to the peripheral nervous system which transmits information from the brain and spinal cord to every other part of the body. The analysis of Electromyography (EMG) signals provides important information to aid in the diagnosis and characterization of Motor Neuron Disease (MND) and any neuromuscular disorders like myopathy and neuropathy.

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“…Our choice was made by considering that the earthquake occurrence time is one of the most reliable and accurate parameters that define a seismic event (Enescu et al, 2006;Telesca et al, 2004). Also, our choice was based on the correlation of earthquake recurrence times.…”

Section: Datamentioning

confidence: 99%

“…Li (lqdzjybzx@126.com) has also been shown in multifractal research on seismic activity that the spectrum of generalised dimensions and the singularity spectrum display characteristic changes before large earthquakes (Caruso et al, 2006;Dimitriu et al, 2000;Nakaya, 2005;Roy and Padhi, 2007). Some results have confirmed that the decrease in fractal dimension, the steepening of curves in the generalised dimension spectrum and the broadening of the value range in the singularity spectrum, can be taken as indicators of an unstable state of an earthquake system before a large rupture (Carpinteri et al, 2009;Goltz, 1997;Kiyashchenko et al, 2004;Lee et al, 2008;Matcharashvili et al, 2000;Radulian and Trifu, 1991;Telesca et al, 2005;Telesca and Lapenna, 2006). However, there are some shortcomings in the research: the mono-fractal dimension is not sufficient to characterise heterogeneous fractals (Grassberger and Procaccia, 1984;Kentz and Schreiber, 1997), while the generalised dimension spectrum and singularity spectrum can only represent a global description of the scaling properties (Chhabra and Jensen, 1989;Grassberger, 1983).…”

Section: Introductionmentioning

confidence: 96%

Local scaling property of seismicity: an example of getting valuable information from complex hierarchical system

2010

Nonlin. Processes Geophys.

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Abstract. In order to get valuable information about the scale invariance in the process of seismogeny of Lijiang M 7.0 earthquake, the scaling property of the interevent time series of the seismic sequences for Lijiang area in China were studied by using the method of the local scaling property, the generalised dimension spectrum, and the correlation dimension. It is found that there is a clear characteristic variation of local scaling property prior to Lijiang M 7.0 earthquake while there is no characteristic variation of the generalised dimension spectrum and the correlation dimension. The reason for producing this phenomenon is that the fractal seismic system is a complex hierarchical system. For such a system, searching for a relevant choice in application of the three methodologies is needed. Compared with the generalised dimension spectrum and mono-fractal dimension which focus on the global description of the scaling properties of fractal objects, the local scaling property emphasizes the local features, and can give the local information of the singularity of the fractal system, therefore, it is easier for us to get valuable information from complex hierarchical structure with this method.

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Mono- and multi-fractal investigation of scaling properties in temporal patterns of seismic sequences

Cited by 125 publications

References 29 publications

Constraining on the Stationarity of Signal with Time-Frequency Surrogates to Enhance the Reliability of Singularity Spectrum Attributes of Random Seismic Noise Wavefield

Constraining on the Stationarity of Signal with Time-Frequency Surrogates to Enhance the Reliability of Singularity Spectrum Attributes of Random Seismic Noise Wavefield

Chaos Based Quantitative Electro-Diagnostic Marker for Diagnosis of Myopathy, Neuropathy and Motor Neuron Disease

Local scaling property of seismicity: an example of getting valuable information from complex hierarchical system

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