The earthquakes network: the role of cell size

Lotfi, Nastaran; Darooneh, Amir H.

doi:10.1140/epjb/e2011-20623-x

Cited by 31 publications

(15 citation statements)

References 11 publications

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“…6. This is consistent with the finding that there is no characteristic cutoff length for interactions between events [20,23], corroborated by Fig. 7, showing the number of links a network has at a given distance as a fraction of the number of links that are possible from choosing any two nodes in the potential network.…”

Section: Resultssupporting

confidence: 90%

“…Figure 8 shows that the networks resulting from our procedure are highly assortative with assortativity generally increasing with r c . The finding of a positive correlation between the degree of a node and the degree of its neighbors is consistent with an analogous finding [20] with Iranian data, using a different technique from ours [18]. For comparison we show the assortativity obtained by using time-shuffled 5)] for a wide range of r c , with assortativity A generally increasing with r c .…”

Section: Resultssupporting

confidence: 89%

“…The results do not qualitatively differ when the fineness of the spatial grid is modified, in agreement with analogous work carried out by the authors of Ref. [20], using a different technique from ours [18]. However, 100 km boxes are a more physical choice, as 100 km is on the order of rupture length associated with larger earthquakes [34], which in turn is roughly equivalent to the aftershock zone distance for larger earthquakes (M 7.0) [35].…”

Section: Methodssupporting

confidence: 88%

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Earthquake networks based on similar activity patterns

2012

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Earthquakes are a complex spatiotemporal phenomenon, the underlying mechanism for which is still not fully understood despite decades of research and analysis. We propose and develop a network approach to earthquake events. In this network, a node represents a spatial location while a link between two nodes represents similar activity patterns in the two different locations. The strength of a link is proportional to the strength of the cross correlation in activities of two nodes joined by the link. We apply our network approach to a Japanese earthquake catalog spanning the 14-year period 1985-1998. We find strong links representing large correlations between patterns in locations separated by more than 1000 kilometers, corroborating prior observations that earthquake interactions have no characteristic length scale. We find network characteristics not attributable to chance alone, including a large number of network links, high node assortativity, and strong stability over time.

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Section: Resultssupporting

confidence: 90%

Section: Resultssupporting

confidence: 89%

Section: Methodssupporting

confidence: 88%

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Earthquake networks based on similar activity patterns

2012

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“…Nowadays, the complex networks theory appears as a useful method for studying many phenomena in various disciplines of science and technology [35][36][37][38]. There are many systems in nature which can be modeled by complex networks such as neural networks [39], cells [40], internet [41,42], earthquakes [43][44][45][46][47][48] and climate network [49][50][51]. Every network is characterized by knowing the nodes and how they are connected to one other.…”

Section: Complex Networkmentioning

confidence: 99%

The network structure affects the fixation probability when it couples to the birth-death dynamics in finite population

2021

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The study of evolutionary dynamics on graphs is an interesting topic for researchers in various fields of science and mathematics. In systems with finite population, different model dynamics are distinguished by their effects on two important quantities: fixation probability and fixation time. The isothermal theorem declares that the fixation probability is the same for a wide range of graphs and it only depends on the population size. This has also been proved for more complex graphs that are called complex networks. In this work, we propose a model that couples the population dynamics to the network structure and show that in this case, the isothermal theorem is being violated. In our model the death rate of a mutant depends on its number of neighbors, and neutral drift holds only in the average. We investigate the fixation probability behavior in terms of the complexity parameter, such as the scale-free exponent for the scale-free network and the rewiring probability for the small-world network.

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“…Since there is no a priori principle for determining the cell size, it is of crucial importance to clarify how change of the cell size affects the network characteristics. This problem has been studied in recent works (Abe and Suzuki, 2009 1 ; Lotfi and Darooneh, 2012). It has been found (Abe and Suzuki, 2009) that the clustering 10 coefficient, C (its definition to be given in the next section), converges to a specific value…”

mentioning

confidence: 97%

<i>C</i> ≈ 0.85 scaling and the universal clustering structure of earthquake networks

Abe

Suzuki

2014

Preprint

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Abstract. Earthquake network describes the complexity of seismicity both qualitatively and quantitatively. The procedure of constructing an earthquake network contains as a single parameter the size of the cells, into which a geographical region under consideration is divided. Then, the characteristics of the network depend on the cell size, in general. Here, the dependency of the clustering coefficient, C, of network on the cell size is studied. Remarkably, C of the earthquake networks constructed from the seismic data taken from California, Japan, and Iran well coincide for each value of the scaled dimensionless cell size. It is found that the networks in California and Japan are three-dimensional, whereas the one in Iran is rather two-dimensional. In addition, the values of C of all these three networks monotonically converge to C ≈ 0.85 as the scaled dimensionless cell size increases, highlighting a universal aspect of the concept of earthquake network.

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The earthquakes network: the role of cell size

Cited by 31 publications

References 11 publications

Earthquake networks based on similar activity patterns

Earthquake networks based on similar activity patterns

The network structure affects the fixation probability when it couples to the birth-death dynamics in finite population

<i>C</i> ≈ 0.85 scaling and the universal clustering structure of earthquake networks

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The earthquakes network: the role of cell size

Cited by 31 publications

References 11 publications

Earthquake networks based on similar activity patterns

Earthquake networks based on similar activity patterns

The network structure affects the fixation probability when it couples to the birth-death dynamics in finite population

&lt;i&gt;C&lt;/i&gt; ≈ 0.85 scaling and the universal clustering structure of earthquake networks

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<i>C</i> ≈ 0.85 scaling and the universal clustering structure of earthquake networks