Comparison of seismicity declustering methods using a probabilistic measure of clustering

Talbi, Abdelhak; Nanjo, Kazuyoshi; Satake, Kenji; Zhuang, Jiancang; Hamdache, Mohamed

doi:10.1007/s10950-013-9371-6

Cited by 24 publications

(19 citation statements)

References 45 publications

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“…Figure 2 shows the spatial distribution of the Swiss seismicity. In our study, we do not consider earthquake depths, in agreement with Talbi et al (2013) and Luen and Starks (2012).…”

Section: Datamentioning

confidence: 58%

“…Regarding the southern California catalogue, we considered two different sets of Reasenberg's parameters (s min , s max , p 1 , r fact ): (i) P1 = (5, 150, 0.9, 6), this set of parameters was used by Talbi et al (2013) to investigate the declustered southern California seismicity from 1932 to 2010; (ii) P2 = (1, 10, 0.95, 10), this set represents the default parameters in the ZMAP software and it was used to decluster the southern California seismicity by Luen and Starks (2012). Regarding the Swiss catalogue, in addition to P1 and P2 we considered also the following set of Reasenberg's parameters, P3 = (2, 10, 0.99, 10) that was used by Faenza et al (2009) to decluster the seismicity of central Europe.…”

Section: Data Analysis and Discussionmentioning

confidence: 99%

“…In some cases, even if a strong earthquake has not occurred, it is possible to observe series of events called ''earthquake swarms'', that are also characterized by a clustering degree enhanced with respect to the background seismicity. However, since the background and the clustered seismicity are superimposed in time and space (Touati et al 2009(Touati et al , 2011, their effective discrimination is not an easy task and the search for an optimal separation technique is still challenging, especially when a working definition of aftershocks and mainshocks has not been firmly stated (Talbi et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Due to such variety of models, Talbi et al (2013) proposed a clustering measure that rates each declustering algorithm according to its adopted reference background model rather than evaluating the performance of each method according to a definition of ''aftershock'' or ''mainshock'', which is not objectively well assessed yet (Talbi et al 2013). In particular, each declustering algorithm is rated by measuring the deviation between its output and the hypothetical background model it assumes, thus allowing comparing the performance of different declustering algorithms, regardless the background assumptions (Talbi et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…In particular, each declustering algorithm is rated by measuring the deviation between its output and the hypothetical background model it assumes, thus allowing comparing the performance of different declustering algorithms, regardless the background assumptions (Talbi et al 2013). Luen and Starks (2012), in order to verify the common assumption that a declustered catalogue is represented by a spatially inhomogeneous and temporally homogeneous Poisson process (SITHP) model, found that the 1932-1971 southern California catalogue declustered by means of Gardner and Knopoff's (1974) method is not fit by a SITHP model for events with magnitude M C 3.8; and Reasenberg's (1985) declustering method applied to southern California seismicity produces catalogues inconsistent with SITHP model, even for events with magnitude M C 4.0.…”

Section: Introductionmentioning

confidence: 99%

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Comparing seismicity declustering techniques by means of the joint use of Allan Factor and Morisita index

Telesca

Lovallo²,

Golay

et al. 2015

Stoch Environ Res Risk Assess

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In this paper, we propose to compare different declustering methods on the basis of the time-correlation and the space-clustering of the residual earthquake catalog after the declustering techniques have been applied. To this aim, we applied two point process clustering measures, the Allan Factor and the Morisita Index, for the identification and quantification of temporal correlation and spatial clustering in point processes, respectively. We used our joint space-time approach to study the earthquake spacetime point processes of southern California and Switzerland with surrounding area, declustered by using the method of Gardner and Knopoff (with Grünthal and Uhmhammer window) and that of Reasenberg (with different setting parameters). Our results show that the residual declustered catalog is still characterized by timecorrelated structures at long timescales; however, the cutoff timescale that is the lowest timescale above which the time-correlation is visible is higher with the Reasenberg method while is smaller with the Gardner and Knopoff method with Grünthal window. The space-clustering analysis performed by means of the Morisita Index suggests that the declustering technique effectively reduces the spatial clustering of the seismicity of Switzerland, but does not change the spatial properties of the residual seismic catalogue of the southern California.

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“…Figure 2 shows the spatial distribution of the Swiss seismicity. In our study, we do not consider earthquake depths, in agreement with Talbi et al (2013) and Luen and Starks (2012).…”

Section: Datamentioning

confidence: 58%

Section: Data Analysis and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparing seismicity declustering techniques by means of the joint use of Allan Factor and Morisita index

Telesca

Lovallo²,

Golay

et al. 2015

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

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Spatiotemporal distribution of Oklahoma earthquakes: Exploring relationships using a nearest‐neighbor approach

Vasylkivska

Huerta

2017

JGR Solid Earth

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Determining the spatiotemporal characteristics of natural and induced seismic events holds the opportunity to gain new insights into why these events occur. Linking the seismicity characteristics with other geologic, geographic, natural, or anthropogenic factors could help to identify the causes and suggest mitigation strategies that reduce the risk associated with such events. The nearest‐neighbor approach utilized in this work represents a practical first step toward identifying statistically correlated clusters of recorded earthquake events. Detailed study of the Oklahoma earthquake catalog's inherent errors, empirical model parameters, and model assumptions is presented. We found that the cluster analysis results are stable with respect to empirical parameters (e.g., fractal dimension) but were sensitive to epicenter location errors and seismicity rates. Most critically, we show that the patterns in the distribution of earthquake clusters in Oklahoma are primarily defined by spatial relationships between events. This observation is a stark contrast to California (also known for induced seismicity) where a comparable cluster distribution is defined by both spatial and temporal interactions between events. These results highlight the difficulty in understanding the mechanisms and behavior of induced seismicity but provide insights for future work.

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Seismic damage accumulation in multiple mainshock–aftershock sequences

Iervolino

Chioccarelli

Suzuki

2020

Earthq Engng Struct Dyn

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Earthquakes are generally clustered, both in time and space. Conventionally, each cluster is made of: foreshocks, the mainshock, and aftershocks. Seismic damage can possibly accumulate because of the effects of multiple earthquakes in one cluster and/or because the structure is unrepaired between different clusters. Typically, the performance-based earthquake engineering (PBEE) framework neglects seismic damage accumulation. This is because: (a) probabilistic seismic hazard analysis (PSHA) only refers to mainshocks and (b) classical fragility curves represent the failure probability in one event, of given intensity, only. However, for life-cycle assessment, it can be necessary to account for the building-up of seismic losses because of damage in multiple events. It has been already demonstrated that a Markovian model (i.e., a Markov chain), accounting for damage accumulation in multiple mainshocks, can be calibrated maintaining PSHA from the classical PBEE framework, and replacing structural fragility with a set of statedependent fragility curves. In fact, the Markov chain also works when damage accumulates in multiple aftershocks from a single mainshock of known magnitude and location, if aftershock-PSHA replaces classical PSHA. Herein this model is extended further developing a Markovian model that accounts, at the same time, for damage accumulation: (i) within any mainshock-aftershock seismic sequence and (ii) among multiple sequences. The model is illustrated through applications to a series of six-story reinforced-concrete moment-resisting-frame buildings designed for three sites with different seismic hazard levels in Italy. The time-variant reliability assessment results are compared to the classical PBEE approach and the accumulation model that only considers mainshocks, so as to address the relevance of aftershocks for life-cycle assessment.

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Comparison of seismicity declustering methods using a probabilistic measure of clustering

Cited by 24 publications

References 45 publications

Comparing seismicity declustering techniques by means of the joint use of Allan Factor and Morisita index

Comparing seismicity declustering techniques by means of the joint use of Allan Factor and Morisita index

Spatiotemporal distribution of Oklahoma earthquakes: Exploring relationships using a nearest‐neighbor approach

Seismic damage accumulation in multiple mainshock–aftershock sequences

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