Time varying seismicity in Greece: Hurst's analysis and Monte Carlo simulation applied to a new earthquake catalogue for Greece

Xu, Yebang; Burton, Paul W.

doi:10.1016/j.tecto.2006.03.006

Cited by 24 publications

(14 citation statements)

References 11 publications

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“…• The results confirm previous findings for other geographic regions (Xu and Burton, 2006; in the sense that the occurrence of seismic events is not random, but shows time clustering behavior. In particular, the existence of two resonance scales at about 100 and 150 events where seismicity exhibits higher correlations were found.…”

Section: Discussionsupporting

confidence: 89%

“…This indicates that the seismic events in the Southern Mexico region exhibit persistent dynamics. This is, as occurring in distinct seismic regions (Xu and Burton, 2006;, the sequence of seismicity is not random, but displays time clustering. In this way, a seismic event is accompanied by many other events for which the interevent period is either similarly shorter or larger.…”

Section: Temporal Variations Of the Hurst Exponentmentioning

confidence: 96%

“…An advantage of these methods is that the strength of correlations can be quantified in terms of a scaling (Hurst) exponent derived from a power-law function that provides information about serial correlations in sequences of interevent periods. Empirical results showed that sequences of interevent periods are persistent, implying that the occurrence of seismic events is not random but governed by stochastic mechanisms with long-term memory (i.e., time-clustering) effects (Xu and Burton, 2006;. Other studies based on the California seismicity catalogue have indicated that the sequence of seismic events cannot be described by a simple or unique statistical model since the phenomenon shows a variety of complex behaviors (Telesca et al, 2004;Jiménez, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Temporal and spatial variations of seismicity scaling behavior in Southern México

Alvarez-Ramı́rez

Echeverría

Ortiz-Cruz

et al. 2012

Journal of Geodynamics

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Section: Discussionsupporting

confidence: 89%

Section: Temporal Variations Of the Hurst Exponentmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temporal and spatial variations of seismicity scaling behavior in Southern México

Alvarez-Ramı́rez

Echeverría

Ortiz-Cruz

et al. 2012

Journal of Geodynamics

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“…The process has long memory and is persistent, when H is greater than 0.5, and is anti-persistent when H is smaller than 0.5. This parameter has been used to analyse long memory of natural (e.g., Correig et al 1997;Xu and Burton 2006;Gkarlaouni et al 2017) and induced seismic processes (e.g., Węglarczyk and Lasocki 2009). Here, we investigate the long memory property in the interevent time series.…”

Section: Methods and Data Used Methodsmentioning

confidence: 99%

Non-stationarity and internal correlations of the occurrence process of mining-induced seismic events

2017

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A point process, e.g., the seismic process, is potentially predictable when it is non-stationary, internally correlated or both. In this paper, an analysis of the occurrence process of mining-induced seismic events from Rudna copper mine in Poland is presented. Stationarity and internal correlation are investigated in complete seismic time series and segmentally in subseries demonstrating relatively stable seismicity rates. It is shown that the complete seismic series are non-stationary; however, most of their shorter subseries become stationary. In the stationary subseries, the distribution of interevent time is closer to the exponential distribution, which is characteristic for the Poisson process. However, in most of these subseries, the differences between the interevent time and Poisson distributions are still significant, revealing correlations among seismic events.

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“…There are numerous papers indicating the non-Poissonian character of tectonic (e.g. Shlien and Toksöz 1970;Vere-Jones 1970;Kiremidjian and Anagnos 1984;Cornell and Winterstein 1988;Parvez and Ram 1997;Lana et al 2005;Xu and Burton 2006;Chang et al 2006;Jimenez 2011;Martin-Montoya et al 2015) as well as anthropogenic seismic processes (e.g. Lasocki 1992;Weglarczyk and Lasocki 2009;Marcak 2013).…”

Section: Introductionmentioning

confidence: 99%

Interval Estimation of Seismic Hazard Parameters

Orlecka‐Sikora

Lasocki

2016

Pure Appl. Geophys.

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Abstract-The paper considers Poisson temporal occurrence of earthquakes and presents a way to integrate uncertainties of the estimates of mean activity rate and magnitude cumulative distribution function in the interval estimation of the most widely used seismic hazard functions, such as the exceedance probability and the mean return period. The proposed algorithm can be used either when the Gutenberg-Richter model of magnitude distribution is accepted or when the nonparametric estimation is in use. When the Gutenberg-Richter model of magnitude distribution is used the interval estimation of its parameters is based on the asymptotic normality of the maximum likelihood estimator. When the nonparametric kernel estimation of magnitude distribution is used, we propose the iterated bias corrected and accelerated method for interval estimation based on the smoothed bootstrap and secondorder bootstrap samples. The changes resulted from the integrated approach in the interval estimation of the seismic hazard functions with respect to the approach, which neglects the uncertainty of the mean activity rate estimates have been studied using Monte Carlo simulations and two real dataset examples. The results indicate that the uncertainty of mean activity rate affects significantly the interval estimates of hazard functions only when the product of activity rate and the time period, for which the hazard is estimated, is no more than 5.0. When this product becomes greater than 5.0, the impact of the uncertainty of cumulative distribution function of magnitude dominates the impact of the uncertainty of mean activity rate in the aggregated uncertainty of the hazard functions. Following, the interval estimates with and without inclusion of the uncertainty of mean activity rate converge. The presented algorithm is generic and can be applied also to capture the propagation of uncertainty of estimates, which are parameters of a multiparameter function, onto this function.Key words: Aggregated uncertainty in the activity rate and magnitude, magnitude cumulative distribution function, interval estimation of seismic hazard functions, resampling methods, bootstrap, jackknife.

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Time varying seismicity in Greece: Hurst's analysis and Monte Carlo simulation applied to a new earthquake catalogue for Greece

Cited by 24 publications

References 11 publications

Temporal and spatial variations of seismicity scaling behavior in Southern México

Temporal and spatial variations of seismicity scaling behavior in Southern México

Non-stationarity and internal correlations of the occurrence process of mining-induced seismic events

Interval Estimation of Seismic Hazard Parameters

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