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How soon would the next mega-earthquake occur in Japan? 被引量:1
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作者 alexey lyubushin 《Natural Science》 2013年第8期1-7,共7页
The problem of seismic danger estimate in Japan after Tohoku mega-earthquake 11 March of 2011 is considered. The estimates are based on processing low-frequency seismic noise wave-forms from broadband network F-net. A... The problem of seismic danger estimate in Japan after Tohoku mega-earthquake 11 March of 2011 is considered. The estimates are based on processing low-frequency seismic noise wave-forms from broadband network F-net. A new method of dynamic estimate of seismic danger is used for this problem. The method is based on calculating multi-fractal properties and minimum entropy of squared orthogonal wavelet coefficients for seismic noise. The analysis of the data using notion of “spots of seismic danger” shows that the seismic danger in Japan remains at high level after 2011. 03. 11 within north-east part of Philippine plate—at the region of Nankai Though which traditionally is regarded as the place of strongest earthquakes. It is well known that estimate of time moment of future shock is the most difficult problem in earthquake prediction. In this paper we try to find some peculiarities of the seismic noise data which could extract future danger time interval by analogy with the behavior before Tohoku earthquake. Two possible precursors of this type were found. They are the results of estimates within 1-year moving time window: based on correlation between 2 mean multi-fractal parameters of the noise and based on cluster analysis of annual clouds of 4 mean noise parameters. Both peculiarities of the noise data extract time interval 2013-2014 as the danger. 展开更多
关键词 SEISMIC Noise MULTI-FRACTAL ANALYSIS Wavelet-Based Minimum Normalized Entropy Cluster ANALYSIS EARTHQUAKE Prediction Dynamic Estimate of SEISMIC Danger
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Prognostic properties of low-frequency seismic noise
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作者 alexey lyubushin 《Natural Science》 2012年第8期659-666,共8页
The prognostic properties of four low-frequency seismic noise statistics are discussed: multi- fractal singularity spectrum support width, wavelet-based smoothness index of seismic noise waveforms, minimum normalized ... The prognostic properties of four low-frequency seismic noise statistics are discussed: multi- fractal singularity spectrum support width, wavelet-based smoothness index of seismic noise waveforms, minimum normalized entropy of squared orthogonal wavelet coefficients and index of linear predictability. The proposed methods are illustrated by data analysis from broad-band seismic network F-net in Japan for more than 15 years of observation: since the beginning of 1997 up to 15 of May 2012. The previous analysis of multi-fractal properties of low-frequency seismic noise allowed a hypothesis about approaching Japan Islands to a future seismic catastrophe to be formulated at the middle of 2008. The base for such a hypothesis was statistically significant decreasing of multi-fractal singularity spectrum support width mean value. The peculiarities of correlation coefficient estimate within 1 year time window between median values of singularity spectra support width and generalized Hurst exponent allowed to make a decision that starting from July of 2010 Japan come to the state of waiting strong earthquake. This prediction of Tohoku mega-earthquake, initially with estimate of lower magnitude as 8.3 only (at the middle of 2008) and further on with estimate of the time beginning of waiting earthquake (from the middle of 2010) was published in advance in a number of scientific articles and abstracts on international conferences. It is shown that other 3 statistics (except singularity spectrum support width) could extract seismically danger domains as well. The analysis of seismic noise data after Tohoku mega-earthquake indicates increasing of probability of the 2nd strong earthquake within the region where the north part of Philippine sea plate is approaching island Honshu (Nankai Trough). 展开更多
关键词 SEISMIC Noise Mutifractal ANALYSIS Wavelet ANALYSIS SMOOTHNESS Index Entropy PREDICTABILITY Earthquake Prediction
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