Declustering of Iran earthquake catalog (1983–2017) using the epidemic-type aftershock sequence (ETAS) model

Davoudi, Nader; Tavakoli, Hamid; Zaré, Mehdi; Jalilian, Abdollah

doi:10.1007/s11600-018-0211-5

Cited by 12 publications

(3 citation statements)

References 31 publications

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“…Fungsi intensitas bersyarat model ETAS dapat digunakan untuk mengetahui aktivitas gempa bumi susulan. Model ETAS diterapkan pada kejadian gempa bumi di wilayah Iran dengan data dari 1983-2017 dengan ambang batas magnitude 4 SR [11]. Sementara itu, untuk kejadian gempa bumi di Indonesia, model ETAS diterapkan pada analisis gempa bumi di Sumatera [12] dan di area pantai selatan Pulau Jawa [7] yang menghasilkan prakiraan intensitas gempa bumi di kedua wilayah tersebut.…”

Section: Pendahuluanunclassified

Model ETAS Spatio-Temporal pada Analisis Pemetaan Intensitas Kegempaan di Wilayah Sumatera

Wijaya¹

2023

Jambura J. Math

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High seismic activity forces Indonesia to mitigate natural disasters to minimize the impact of these disasters. One of the mitigation efforts that can be done is to know the possibility of an earthquake occurring in an area. Earthquakes, which are random phenomena, cannot be determined with certainty. Therefore, studies in seismological statistics can be used. One of the studies in seismological statistics that discusses the occurrence of earthquakes is the epidemic-type aftershock sequence (ETAS) model. This model describes the main seismic activity followed by aftershocks. The ETAS model, which only considers the parameters of time and event magnitude, was developed into a Spatio-temporal ETAS model, which also describes spatial parameters or locations. This study used the Spatio-temporal ETAS model to analyze the Sumatra region’s seismic activity from 2000-2022. Earthquake data sources are obtained from the United State Geological Survey (USGS). Based on data analysis, the Spatio-temporal ETAS model expressed in terms of the conditional intensity function shows that earthquakes that occur in the Sumatera region with a large magnitude tend to produce a large conditional intensity function as well. The spatial component of the spatio-temporal ETAS model can be explained by mapping the peak ground acceleration (PGA). The results of the PGA mapping show that seismic activity in the Sumatera region is prone to occur along the north and west coasts of the Sumatra region, with PGA values ranging from 0.2 g to 0.9 g. The larger the PGA value means, the higher the risk of an earthquake occurring in the area marked with a reddish-yellow dot or contour.

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Section: Pendahuluanunclassified

Model ETAS Spatio-Temporal pada Analisis Pemetaan Intensitas Kegempaan di Wilayah Sumatera

Wijaya¹

2023

Jambura J. Math

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“…(2018) for the nearest‐neighbor method and Davoudi et al. (2018), Zhuang et al. (2005), and Llenos and Michael (2020) for the stochastic declustering method.…”

Section: Introductionmentioning

confidence: 99%

“…Among the frequently used alternatives to window-based methods, we focus on two recently proposed declustering algorithms: the nearest-neighbor method by Ben-Zion (2013, 2016) and the stochastic declustering method by Zhuang et al (2002Zhuang et al ( , 2004 and Zhuang (2006). They have been the subject of several recent papers to which the readers can refer for additional details, for example, Peresan and Gentili (2018), Zhang andShearer (2016), andMartínez-Garzón et al (2018) for the nearest-neighbor method and Davoudi et al (2018), Zhuang et al (2005), and Llenos and Michael (2020) for the stochastic declustering method. Both methods are data driven and can be satisfactorily applied to decompose the seismic catalog into background seismicity and sequences of clustered earthquakes.…”

Section: Introductionmentioning

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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A Model Based on Fuzzy C-Means with Density Peak Clustering for Seismicity Analysis of Earthquake Prone Regions

Sharma

Nanda

Vijay

2021

Advances in Intelligent Systems and Computing

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Declustering of Iran earthquake catalog (1983–2017) using the epidemic-type aftershock sequence (ETAS) model

Cited by 12 publications

References 31 publications

Model ETAS Spatio-Temporal pada Analisis Pemetaan Intensitas Kegempaan di Wilayah Sumatera

Model ETAS Spatio-Temporal pada Analisis Pemetaan Intensitas Kegempaan di Wilayah Sumatera

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

A Model Based on Fuzzy C-Means with Density Peak Clustering for Seismicity Analysis of Earthquake Prone Regions

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