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Information Journal Paper

Title

Determination of seismic parameters of Iranian earthquakes by network theory

Pages

  117-130

Abstract

Complex Network theory has appeared as a suitable tool for studying complex phenomena. earthquakes show spatio-temporal complex behavior that can be studied using Complex Networks, which enables us to recognize the global properties of earthquakes. It is so hard to consider all factors affecting the movement of faults and put them into a compact mathematical equation in order to describe the earthquake phenomenon. In Complex Network theory, we do not need to know details of the fault system. Using the network method, knowing  the magnitude, time of occurrence and location of seismic events, we are able to understand several aspects of the earthquake phenomenon.     Studies on earthquakes using the network method are based on how the network is constructed. It was tried to show that the main statistical features of earthquake phenomena in any region can be retrieved from the associated earthquake network.    In this research, the Baiesi-Paczuski (2004) network model used to investigate the earthquake network. Earthquake epicenters play the role of network nodes and the relationship between both nodes was established by the relationship defined by Baiesi-Paczuski.    In this project, by constructing the network, the parameters of seismicity (a-value, b-value), fractal dimension and correlation ratio in the Iran region. In the first attempt we drew out the Gutenberg–Richter law from the earthquakes network and the Omori law from the Baiesi-Paczuski network model.    We have used data recorded from 1996 to 2020 data at all the broadband and short-period stations of the Iranian Seismological Center. The results showed that b-value≈ 0.93 and a-value≈6.9  and. Also, the results showed that for df≈1.56  each earthquake with a certain magnitude, the aftershock rates follow the Omori law with the line gradient of p≈-1 and the earthquake degree distribution function in Iran from the power relationship ((P(k)~kγ) with a γ = 1.59. In addition, the distribution function showed that the correlation relation is also a power relationship.

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