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The earthquake network: the best time scale for network construction

Lotfi Nastaran

Acta Geophysica

2023

Vol. 71, no. 6

2565--2571

Scientists mapped the seismic time series into networks by considering the geographical location of events as nodes and establishing links between the nodes with different rules. Applying the successively defined models to construct the networks of seismic data, a variety of features of earthquake networks are detected (scale-free and small-world structures). Network construction models had changed in detail to optimize the performance of the verification of the minimum geographical size defined for the node. In all the studies, people try to use large data sets like years of data to ensure their results are good enough. In this work, by proposing the temporal network construction and employing the small-worldness property for data from Iran and California, we could achieve the minimum time scale needed for the best results. We verified the importance of this scale by analyzing two significant centrality measures (degree centrality and PageRank) introduced in the concept of earthquake network.

Earthquake network construction models: from Abe‑Suzuki to a multiplex approach

Lotfi Nastaran

Acta Geophysica

2023

Vol. 71, no. 3

1111--1117

From the early stage of seismological research, a complex network is one of the statistical methods to investigate the complexity of earthquake systems. The benefit of using this method is to inspect the systems with minimum information about their entities and corresponding interactions. Achieving a high interest in studying the seismic events using the complex network resulted in defining models to map the seismic data into networks. Application of these models to the seismic data sets in nonidentical geographical regions has yielded promising results independent of time and location. In this review, we bring in the recent famous models varying from monolayer to multiplex and compare their proficiency in capturing the complexity of the seismicity by using two data sets from Iran and California.