Modelling background seismicity components identified by nearest neighbour and stochastic declustering approaches: the case of Northeastern Italy

Benali, Amel; Peresan, Antonella; Varini, Elisa; Talbi, Abdelhak

doi:10.1007/s00477-020-01798-w

Cited by 12 publications

(7 citation statements)

References 34 publications

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“…The obtained results highlight non-stationarity of seismic activity, at a time-scale of several years and up to decades, in agreement with earlier findings by Benali et al (2020), an element that should be taken into account for improving local seismic hazard assessment. The regions and time intervals identified in this study, which display homogeneous features of seismic activity, may supply valuable information toward timedependent seismic hazard assessment (e.g., Kossobokov et al, 2015 and references therein), while providing new constraints for earthquakes forecasting in Northeastern Italy.…”

Section: Discussionsupporting

confidence: 90%

“…Although the temporal pattern of activity rate changes identified in this study reflects trends at the sub-regions scale, this result appears compatible with the observation of a period of rather low background seismicity rate (i.e. for the declustered catalog) ongoing since more than a decade, detected by Benali et al (2020) for the whole FVG region.…”

Section: Discussionsupporting

confidence: 89%

“…Several efforts have been already devoted to explore the different components of seismicity in the study region, namely the clustering properties Gentili, 2018, 2020;Varini et al, 2020) as well as the temporal variability of background seismicity component (Benali et al, 2020). Here we investigate changes in seismicity as a whole, without differentiating between clustered and background components.…”

Section: Introductionmentioning

confidence: 99%

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Unified Scaling Law for Earthquakes: Space-Time Dependent Assessment in Friuli-Venezia Giulia Region

Nekrasova

Peresan

2021

Front. Earth Sci.

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The concept of the Unified Scaling Law for Earthquakes (USLE), which generalizes the Gutenberg-Richter relationship making use of the fractal distribution of earthquake sources in a seismic region, is applied to seismicity in the Friuli-Venezia Giulia region, FVG (Northeastern Italy) and its surroundings. In particular, the temporal variations of USLE coefficients are investigated, with the aim to get new insights in the evolving dynamics of seismicity within different tectonic domains of FVG. To this purpose, we consider all magnitude 2.0 or larger earthquakes that occurred in 1995–2019, as reported in the catalog compiled at the National Institute of Oceanography and Applied Geophysics (OGS catalog), within the territory of its homogeneous completeness. The observed variability of seismic dynamics for three sub-regions of the territory under investigation, delimited based on main geological and tectonic features, is characterized in terms of several moving averages, including: the inter-event time, τ; the cumulative Benioff strain release, Ʃ; the USLE coefficients estimated for moving six-years time intervals, and the USLE control parameter, η. We found that: 1) the USLE coefficients in FVG region are time-dependent and show up correlated; 2) the dynamical changes of τ, Ʃ, and η in the three sub-regions highlight a number of different seismic regimes; 3) seismic dynamics, prior and after the occurrence of the 1998 and 2004 Kobarid (Slovenia) strong main shocks, is characterized by different parameters in the related sub-region. The results obtained for the FVG region confirm similar analysis performed on a global scale, in advance and after the largest earthquakes worldwide. Moreover, our analysis highlights the spatially heterogeneous and non-stationary features of seismicity in the investigated territory, thus suggesting the opportunity of resorting to time-dependent estimates for improving local seismic hazard assessment. The applied methods and obtained parameters provide quantitative basis for developing suitable models and forecasting tools, toward a better characterization of future seismic hazard in the region.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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Unified Scaling Law for Earthquakes: Space-Time Dependent Assessment in Friuli-Venezia Giulia Region

Nekrasova

Peresan

2021

Front. Earth Sci.

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“…The global features of the resulting background seismicity and earthquake clusters turn out to be consistent between the two approaches, though the partitions are slightly different (Benali et al., 2020). Specifically, the statistics of clusters, singles, and fore/aftershocks are quite comparable (Table 1).…”

Section: Discussionmentioning

confidence: 92%

“…Notably the two methods, as applied in this study, have a different definition of background events: while NN assigns to the background seismicity the largest event from each cluster (i.e., the mainshock), SD assigns to it the first event of the cluster (not necessarily the mainshock). Therefore, the declustered catalogs resulted from these two methods may differ, particularly when foreshocks are identified (Benali et al., 2020).…”

Section: Declustering Algorithms Under Examinationmentioning

confidence: 99%

Topological Comparison Between the Stochastic and the Nearest‐Neighbor Earthquake Declustering Methods Through Network Analysis

2020

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Earthquake clustering is a significant feature of seismic catalogs, both in time and space. Several methodologies for earthquake cluster identification have been proposed in the literature in order to characterize clustering properties and to analyze background seismicity. We consider two recent data‐driven declustering techniques, one based on nearest‐neighbor distance and the other on a stochastic point process. These two methods use different underlying assumptions and lead to different classifications of earthquakes into background events and clustered events. We investigated the classification similarities by exploiting graph representations of earthquake clusters and tools from network analysis. We found that the two declustering algorithms produce similar partitions of the earthquake catalog into background events and earthquake clusters, but they may differ in the identified topological structure of the clusters. Especially the clusters obtained from the stochastic method have a deeper complexity than the clusters from the nearest‐neighbor method. All of these similarities and differences can be robustly recognized and quantified by the outdegree centrality and closeness centrality measures from network analysis.

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Generation of synthetic catalog by using Markov chain Monte Carlo simulation and inverse Poisson distribution

Karaca

2021

J Seismol

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Modelling background seismicity components identified by nearest neighbour and stochastic declustering approaches: the case of Northeastern Italy

Cited by 12 publications

References 34 publications

Unified Scaling Law for Earthquakes: Space-Time Dependent Assessment in Friuli-Venezia Giulia Region

Unified Scaling Law for Earthquakes: Space-Time Dependent Assessment in Friuli-Venezia Giulia Region

Topological Comparison Between the Stochastic and the Nearest‐Neighbor Earthquake Declustering Methods Through Network Analysis

Generation of synthetic catalog by using Markov chain Monte Carlo simulation and inverse Poisson distribution

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