Multifractality in seismic sequences of NW Himalaya

Chamoli, Ashutosh; Yadav, R. B. S.

doi:10.1007/s11069-013-0848-y

Cited by 23 publications

(16 citation statements)

References 28 publications

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“…MF-DFA has successfully been applied to fields as diverse as finance and stock markets [43][44][45], seismicity [46][47][48], mineral grade detection [49], climate change [50][51][52][53][54], traffic flow [55,56], speech signal characteristics [57], plant species identification [58], air pollution [59,60], and heart rate dynamics [61]. It is very valuable to introduce this method into the analysis of shoulder-lines.…”

Section: Introductionmentioning

confidence: 99%

Topographic Spatial Variation Analysis of Loess Shoulder Lines in the Loess Plateau of China Based on MF-DFA

Cao

et al. 2017

IJGI

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Abstract:The Loess Plateau in China is internationally known for its unique geographical features and has therefore been studied by many researchers. This research exploits the regional differences in the spatial morphological characteristics of Loess shoulder lines in different landform types as an important basis for geomorphological regionalization. In this study, we used ensemble empirical mode decomposition (EEMD), multi-fractal detrended fluctuation analysis (MF-DFA), and detrended cross-correlation analysis (DCCA) to analyze topographic data series extracted from shoulder lines. Loess shoulder-line land variations series data from the Suide, Ganquan, and Chunhua areas on the Loess Plateau were selected and a combination of the two above-mentioned methods was used to study land variations at these three sample sites. The results revealed differences in the topographic variations of the multi-fractal characteristics and the topographic spatial variation in the Loess shoulder line of the three sample sites. Furthermore, the extent to which the results were affected by noise and the analysis scale differed among the three areas.

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Section: Introductionmentioning

confidence: 99%

Topographic Spatial Variation Analysis of Loess Shoulder Lines in the Loess Plateau of China Based on MF-DFA

Cao

et al. 2017

IJGI

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“…Although the most of the studies of fractal/multifractal characteristics of earthquake sequences have been extensively focused on the space and time domain [7,26,30], very few works have investigated the fractal/multifractal features of the earthquake magnitude series. Lennartz et al [14] studied the long-range correlations of the magnitude sequences in Northern and Southern California by using the detrended fluctuation analysis (DFA) and showed that the temporal fluctuations of magnitudes are characterized by long-term memory.…”

Section: Introductionmentioning

confidence: 99%

Multifractal detrended fluctuation analysis of earthquake magnitude series of Mexican South Pacific Region

Márquez

Ramírez-Rojas

Telesca

2015

Applied Mathematics and Computation

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“…It is widely used when time series has not a simple monofractal scaling behavior, but exhibits more complicated one, when different parts of time series characterized by different scaling exponents: e.g. streamflow series (Zhang et al, 2008), seismic interevent times of earthquakes (Chamoli and Yadav, 2015), seismograms (Padhy, 2006), volcanic signals during pre-and eruptive phases (Telesca et al, 2015).…”

Section: Multifractal Detrended Fluctuation Analysismentioning

confidence: 99%

Fractal analysis of seismoacoustic signals of near-surface sedimentary rocks in Kamchatka

Imashev

Mishchenko

2020

Geofizika (Online)

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We studied, by the mono-and multifractal detrended fluctuation analysis (DFA), time fluctuations in the dynamics of seismoacoustic data, recorded in Karymshina site, which is located in a seismic area of Kamchatka. We took a series of seismoacoustic responses to the regional seismic events with the magnitudes M > 4 for the period 2017-2018. The series was divided into three groups (high, medium and low) based on the amplitude of recorded seismoacoustic response. Background noise segments of the signals demonstrated monofractal behavior similar to white noise by almost constant values of generalized Hurst exponent H q ≈0.5 and very small width of the multifractal spectrum Δa ≈ 0.1. Analysis of the high amplitude seismoacoustic signals with clear P-, S-and coda waves showed that P-and S-waves demonstrate wider multifractal spectrum (Δa P = 0.37, Δa S = 0.35) and range of generalized Hurst exponents H q in comparison with coda wave, characterized by almost constant H q and minimal width of multifractal spectrum (Δa CODA = 0.13). We showed that the properties of the multifractal spectrum could be used in detection of seismic wave arrival, estimation its duration and separation of P-, S-and coda waves. Application of the monofractal DFA in a sliding window showed that the acoustic signal transits from monofractal and uncorrelated background noise (Hurst exponent equals to 0.5) into the long-range dependent state during seismic waves arrival, that is helpful in analysis of the signals, particularly in case of low amplitude acoustic responses, usually demonstrating an unclear waveform. Difference in multifractal spectrum width between the original low amplitude signal and its surrogates, obtained by random shuffling showed that the multifractality in the signal is dominantly due to long-range correlations.

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Multifractality in seismic sequences of NW Himalaya

Cited by 23 publications

References 28 publications

Topographic Spatial Variation Analysis of Loess Shoulder Lines in the Loess Plateau of China Based on MF-DFA

Topographic Spatial Variation Analysis of Loess Shoulder Lines in the Loess Plateau of China Based on MF-DFA

Multifractal detrended fluctuation analysis of earthquake magnitude series of Mexican South Pacific Region

Fractal analysis of seismoacoustic signals of near-surface sedimentary rocks in Kamchatka

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