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Register a new userAvalanches on the Complex Network of Rigan Earthquake, Virtual Seismometer Technique, Criticality and Seismic Cycle
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Added by
Morteza Nattagh Najafi
Publication date
2019
fields
Physics
and research's language is
English
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We base our study on the statistical analysis of the Rigan earthquake 2010 December 20, which consists of estimating the earthquake network by means of virtual seismometer technique, and also considering the avalanche-type dynamics on top of this complex network.The virtual seismometer complex network shows power-law degree distribution with the exponent $gamma=2.3pm 0.2$. Our findings show that the seismic activity is strongly intermittent, and have a textit{cyclic shape} as is seen in the natural situations, which is main finding of this study. The branching ratio inside and between avalanches reveal that the system is at (or more precisely close to) the critical point with power-law behavior for the distribution function of the size and the mass and the duration of the avalanches, and with some scaling relations between these quantities. The critical exponent of the size of avalanches is $tau_S=1.45pm 0.02$. We find a considerable correlation between the dynamical Green function and the nodes centralities.
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Earthquake network is known to be complex in the sense that it is scale-free, small-world, hierarchically organized and assortatively mixed. Here, the time evolution of earthquake network is analyzed around main shocks in the context of the community structure. It is found that the maximum of the modularity measure quantifying existence of communities exhibits a peculiar behavior: its maximum value stays at a large value before a main shock, suddenly drops to a small value at the main shock, and then increases to relax to a large value again relatively slowly. Thus, a main shock absorbs and merges communities to create a larger community, showing how a main shock can be characterized in the complex-network representation of seismicity.
An article for the Springer Encyclopedia of Complexity and System Science
We present a method for locating the seismic event epicenters without assuming an Earth model of the seismic velocity structure, based on the linear relationship between $log R$ and $log t$ (where $R$ is the radius of spherical P wave propagated outwards from the hypocenter, $t$ is the travle-time of the P wave). This relationship is derived from the dimensional analysis and a lot of theoretical or real seismic data, in which the earthquake can be considered to be a point source. Application to 1209 events occurred from 2014 to 2017 in the IASPEI Ground Truth (GT) reference events list shows that our method can locate the correct seismic event epicenters in a simple way. $sim 97.2$ % of seismic epicenters are located with both longitude and latitude errors $in[-0.1^circ, +0.1^circ]$. This ratio can increase if with a finer search grid. As a direct and global-search location, this method may be useful in obtaining the earthquake epicenters occurred in the areas where the seismic velocity structure is poorly known, the starting points or the constraints for other location methods.
Seismic attributes calculated by conventional methods are susceptible to noise. Conventional filtering reduces the noise in the cost of losing the spectral bandwidth. The challenge of having a high-resolution and robust signal processing tool motivated us to propose a sparse time-frequency decomposition while is stabilized for random noise. The procedure initiates by using Sparsity-based adaptive S-transform to regularize abrupt variations in frequency content of the nonstationary signals. Then, considering the fact that a higher amplitude of a frequency component results in a higher signal to noise ratio, an adaptive filter is applied to the time-frequency spectrum which is sparcified previously. The proposed zero adaptive filter enhances the high amplitude frequency components while suppresses the lower ones. The performance of the proposed method is compared to the sparse S-transform and the robust window Hilbert transform in estimation of instantaneous attributes by applying on synthetic and real data sets. Seismic attributes estimated by the proposed method is superior to the conventional ones in terms of its robustness and high resolution image. The proposed approach has a vast application in interpretation and identification of geological structures.
The analysis of the seismic vulnerability of urban centres has received a great attention in the last century. In order to estimate the seismic vulnerability of a densely populated urban area, it would in principle be necessary to develop in-depth analyses for predicting the dynamic behaviour of the individual buildings and their structural aggregation. Such analyses, however, are extremely cost-intensive, require great processing time and above all expertise judgement. It is therefore very useful to define simplified rules for estimating the seismic vulnerability of whole urban areas. In the last decades, the Self-Organized Criticality (SOC) scenario has gained increasing credibility as a mathematical framework for explaining a large number of naturally occurring extreme events, from avalanches to earthquakes dynamics, from bubbles and crises in financial markets to the extinction of species in the evolution or the behaviour of human brain activity. All these examples show the intrinsic tendency common to many phenomena to spontaneously organize into a dynamical critical state, whose signature is the presence of a power law behaviour in the frequency distribution of events. In this context, the Olami-Feder- Christensen (OFC) model, introduced in 1992, has played a key role in modelling earthquakes phenomenology. The aim of the present paper is proposing an agent-based model of earthquake dynamics, based on the OFC self- organized criticality framework, in order to evaluate the effects of a critical sequence of seismic events on a given large urban area during a given interval of time. The further integration of a GIS database within a software environment for agent-based simulations, will allow to perform a preliminary parametric study of these effects on real datasets. The model could be useful for defining planning strategies for seismic risk reduction
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