Dynamics of multifractal and correlation characteristics of the spatio-temporal distribution of regional seismicity before the strong earthquakes

Abstract

Abstract.Investigations of the distribution of regional seismicity and the results of numerical simulations of the seismic process show the increase of inhomogenity in spatiotemporal distribution of the seismicity prior to large earthquakes and formation of inhomogeneous clusters in a wide range of scales. Since that, the multifractal approach is appropriate to investigate the details of such dynamics.Here we analyze the dynamics of the seismicity distribution before a number of strong earthquakes occurred in two seismically active regions of the world: Japan and Southern California. In order to study the evolution of spatial inhomogeneity of the seismicity distribution, we consider variations of two multifractal characteristics: information entropy of multifractal measure generation process and the higher-order generalized fractal dimension of the continuum of the earthquake epicenters. Also we studied the dynamics of the level of spatio-temporal correlations in the seismicity distribution. It is found that two aforementioned multifractal characteristics tend to decrease and the level of spatio-temporal correlations tends to increase before the majority of considered strong earthquakes. Such a tendency can be considered as an earthquake precursory signature.Therefore, the results obtained show the possibility to use multifractal and correlation characteristics of the spatiotemporal distribution of regional seismicity for seismic hazard risk evaluation.

Multifractal analysis for the ulf geomagnetic data during the guam earthquake

“…But, they analyzed the temporal behavior of the spectrum of generalized dimensions (the Dq curve) obtained from the epicenter distributions. The similar work to these has been recently published by Kiyaschenko et al (2003) in order to find out some medium-term earthquake precursors. The epicenter distributions analyzed by Jiang (1993) and Hirabayashi et al (1992) are known to be useful for the medium-term prediction, but the short-term earthquake prediction seems to be possible only with the use of electromagnetic phenomena (Hayakawa and Molchanov, 2002).…”

Multifractal analysis for the ULF geomagnetic data during the 1993 Guam earthquake

“…But, they analyzed the temporal behavior of the spectrum of generalized dimensions (the Dq curve) obtained from the epicenter distributions. The similar work to these has been recently published by Kiyaschenko et al (2003) in order to find out some medium-term earthquake precursors. The epicenter distributions analyzed by Jiang (1993) and Hirabayashi et al (1992) are known to be useful for the medium-term prediction, but the short-term earthquake prediction seems to be possible only with the use of electromagnetic phenomena .…”

Temporal characteristics of seismicity in the Alborz and Zagros regions of Iran, using a multifractal approach

“…Earthquake phenomena, like many other natural fractals, are not completely self-similar or homogeneous and a single fractal dimension is not enough to characterize their fractal properties (Geilikman et al, 1990;Hirata and Imoto, 1991;Goltz, 1997;Mittag, 2003;Kiyashchenko et al, 2003;Enescu et al, 2005Enescu et al, , 2006Telesca and Lapenna, 2006). In such cases we need to extend the idea of fractal dimension by studying the singularity spectrum f(˛q), and the Rényi or generalized correlation dimensions D q .…”

“…Simple or homogeneous fractal models of earthquakes have been quantitatively characterized using the idea of box-counting method (King, 1983;Turcotte, 1986;Smalley et al, 1987;Lei and Kusunose, 1999). In comparison with the usual box dimension, the multifractal dimensions are more efficient to reflect complex geodynamic processes which take place in active tectonic regions (Geilikman et al, 1990;Hirata and Imoto, 1991;Goltz, 1997;Kiyashchenko et al, 2003;Mittag, 2003;Enescu et al, 2005;Telesca and Lapenna, 2006). The application of multifractal concepts in seismicity is the appropriate modeling of the chaotic patterns of earthquake distributions.…”