Fractal structure of spatial distribution of microfracturing in rock

Hirata, Takayuki; Satoh, Takashi; Ito, Keisuke

doi:10.1111/j.1365-246x.1987.tb00732.x

Cited by 319 publications

(178 citation statements)

References 10 publications

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“…In the present study, following Hirata et al (1987), we used the correlation integral method for analysis of the spatial distribution of synthetic seismicity. Uritsky and Troyan (1998) studied the behaviour of fractal dimension, calculated by the box-counting method.…”

Section: Discussionmentioning

confidence: 99%

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The pecularities of shear crack pre-rupture evolution and distribution of seismicity before strong earthquakes

Kiyashchenko

Troyan

2001

Nat. Hazards Earth Syst. Sci.

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Abstract. Several methods are presently suggested for investigating pre-earthquake evolution of the regions of high tectonic activity based on analysis of the seismicity spatial distribution. Some precursor signatures are detected before strong earthquakes: decrease in fractal dimension of the continuum of earthquake epicenters, cluster formation, concentration of seismic events near one of the nodal planes of the future earthquake, and others. In the present paper, it is shown that such peculiarities are typical of the evolution of the shear crack network under external stresses in elastic bodies with inhomogeneous distribution of strength. The results of computer modeling of crack network evolution are presented. It is shown that variations of the fractal dimension of the earthquake epicenters' continuum and other precursor signatures contain information about the evolution of the destruction process towards the main rupture.

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Section: Discussionmentioning

confidence: 99%

“…The gradual decreasing of the correlation exponent of the spatial distribution of acoustic shocks was observed by Hirata et al (1987) during the destruction of granite sample. The correlation exponent is a kind of fractal dimension, which can be calculated for the group of points according to the following algorithm.…”

Section: Introductionmentioning

confidence: 99%

The pecularities of shear crack pre-rupture evolution and distribution of seismicity before strong earthquakes

Kiyashchenko

Troyan

2001

Nat. Hazards Earth Syst. Sci.

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“…Ponomarev et al (1997) found a significant decrease in fractal dimension just before the global instability in the laboratory experiment. Then, the spatial distribution of acoustic emissions of rock fracture experiment was used to obtain a decrease in fractal dimension before the rupture (Jaeger and Cook, 1976;Hirata et al, 1987). The temporal evolution of acoustic emissions was also studied by Hirata (1987), who found that the temporal evolution of acoustic emissions becomes fractal over a range from microfracture to an EQ.…”

Section: The Importance Of Fractal Analysis In the Nonlinear Process mentioning

confidence: 99%

Fractal analysis of ULF electromagnetic emissions in possible association with earthquakes in China

Ida

Yang

et al. 2012

Nonlin. Processes Geophys.

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Abstract. The long-term data (during the period of 1 March 2003 through 31 December 2006) of ULF geomagnetic variations observed at Kashi station (geographic coordinates: 39.5 • N, 76.0 • E) in China have been used to investigate the long-term variation of fractal dimension of ULF emissions. We have studied the changes in fractal dimension in association with several earthquakes around the observation station. It is then found that a significant change (or decrease) in the fractal dimension of the Z component took place before the 1 September 2003 earthquake, which lends a further support to our previous finding based on our improved polarization analysis for the same earthquake. The results obtained are discussed in the contexts of a few aspects (detectability of seismogenic emissions, comparison with previous results by other analysis methods, the importance of fractal analysis in the nonlinear process of the lithosphere and earthquake prediction).

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“…Based on observed data, Hirata (1987) and Yamamoto et al (1993) found that fractures are not randomly distributed in space, but spatially clustered. The spatial clustering patterns could be described by fractals (Hirata, 1987;Yamamoto et al, 1993).…”

Section: Implications and Limitations Of The Data Analysismentioning

confidence: 99%