Prediction of Great Japan Earthquake 11 March of 2011 by analysis of microseismic noise. Does Japan approach the next Mega-EQ?

Transcription

1 Prediction of Great Japan Earthquake 11 March of 2011 by analysis of microseismic noise. Does Japan approach the next Mega-EQ? Alexey Lyubushin Institute of Physics of the Earth Russian Academy of Sciences Moscow Ver. 05 May 2018

2 When and Were the prediction of Tohoku EQ was published before the earthquake Lyubushin A.A. Multi-fractal Properties of Low-Frequency Microseismic Noise in Japan, Book of abstracts of 7th General Assembly of the Asian Seismological Commission and Japan Seismological Society, 2008 Fall meeting, Tsukuba, Japan, November 2008, p.92. Lyubushin A.A. Synchronization Trends and Rhythms of Multifractal Parameters of the Field of Low-Frequency Microseisms Izvestiya, Physics of the Solid Earth, 2009, Vol. 45, No. 5, pp Lyubushin A.A. The Statistics of the Time Segments of Low-Frequency Microseisms: Trends and Synchronization Izvestiya, Physics of the Solid Earth, 2010, Vol., No. 6, pp Lyubushin A.A. Synchronization of multi-fractal parameters of regional and global low-frequency microseisms European Geosciences Union General Assembly 2010, Vienna, of May, 2010, Geophysical Research Abstracts, Vol. 12, EGU , 2010, Lyubushin A., Multifractal Parameters of Low-Frequency Microseisms // V. de Rubeis et al. (eds.), Synchronization and Triggering: from Fracture to Earthquake Processes, GeoPlanet: Earth and Planetary Sciences 1, DOI / _15, Springer-Verlag Berlin Heidelberg, 2010, 8p., Chapter 15, pp Lyubushin A.A. Synchronization phenomena of low-frequency microseisms. European Seismological Commission, nd General Assembly, September 06-10, 2010, Montpelier, France. Book of abstracts, p.124, session ES6. Lyubushin A.A. Cluster Analysis of Low-Frequency Microseismic Noise Izvestiya, Physics of the Solid Earth, 2011, Vol. 47, No. 6, pp (submitted April 26, 2010) Publications after the Tohoku earthquake and about increasing seismic danger at the Nankai Trough. Lyubushin A.A. (2011) Seismic Catastrophe in Japan on March 11, 2011: Long-Term Prediction on the Basis of Low-Frequency Microseisms Izvestiya, Atmospheric and Oceanic Physics, 2011, Vol., No. 8, pp Lyubushin, A. (2012) Prognostic properties of low-frequency seismic noise. Natural Science, Vol.4,No.8A, doi: 10./ns Lyubushin A.A. (2013) Mapping the Properties of Low-Frequency Microseisms for Seismic Hazard Assessment. Izvestiya, Physics of the Solid Earth, 2013, Vol.49, No.1, pp ISSN , DOI: 10.11/S Lyubushin A.A. (2013) Spots of Seismic Danger Extracted by Properties of Low-Frequency Seismic Noise European Geosciences Union General Assembly 2013, Vienna, of April, 2013, Geophysical Research Abstracts, Vol. 15, EGU , Lyubushin, A. (2013) How soon would the next mega-earthquake occur in Japan? Natural Science, Vol.5, No.8A1, 1-7. doi: 10./ns A Lyubushin A.A. (2014) Dynamic estimate of seismic danger based on multifractal properties of low-frequency seismic noise. Natural Hazards, January 2014, Volume 70, Issue 1, pp DOI /s

3 Asian Seismological Commission, Tsukuba, Japan, Nov 2008 The earliest prediction (at the middle of 2008): the magnitude of oncoming catastrophe will be more than 8.3.

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5 The latest prediction: dangerous time for waiting catastrophe begins at the middle of 2010

6 Positions of 78 seismic stations of broadband network F-net in Japan. N, deg , M = , M = E, deg 148

7 Main sources of lowfrequency microseisms Atmospheric Pressure Variations Oceanic Waves Low-Frequency Microseismic Noise: Slow Earthquakes, Creep 2 min Periods 1000 min

8 Multi-fractal singularity spectrum Measure of the random signal variability at the time interval [ t δ/2, t + δ/2 ] Multi-fractal singularity spectrum F (α) and its parameters: α - support width and α - generalized Hurst exponent. 1.0 F (α) - fractal dimensionality of the set of time moments t for which h(t) = α µ(t,δ ) δ h(t) δ α = α max α min t δ α min α α max α

9 Minimum normalized entropy of squared orthogonal wavelet coefficients distribution Minimum normalized entropy : N En = p log( p ) / log( N) min k = 1 k N En 1, pk = ck / c j, j= 1 c j k - orthogonal wavelet coefficients, minimum is taken by wavelets from Daubechies family.

10 Data transform Initial LHZ-records, t = 1 sec t = 1 min Seismic Noise, t = 1 min Downsampling Detrending Donoho share Log(Variance) Kurtosis Estimates within adjacent daily time intervals, t = 1 day α Linear Predictability Index Spectral Exponent α α min Smoothness Index Normalized Entropy

11 Graphics of seismic noise of the length 1 day with sampling time step 1 minute after removing tidal trends High α Low normalized entropy En Low danger, high variability of stochastic behavior types, a lot of low-frequency spikes because of mutual movements of non-consolidated small blocks of the Earth's crust, energy is not accumulated. Low α High normalized entropy En High danger, the behavior of the seismic noise is much more uniform, small blocks of the Earth's crust are consolidated, the energy could be accumulated. α = 0.249, En = α = 0.821, En = α = 0.154, En = α = 0.757, En = α = 0.207, En = α = 0.809, En = Time, minutes Time, minutes

12 Maps of multi-fractal singularity spectra support width α. Low α values ( blue and violet regions ) indicate synchronization and danger α α N, deg N, deg E, deg E, deg From the beginning of 1997 up to 25 of Sept 2003: the area of future seismic catastrophe is characterized by relatively low α and it is not split into North and South parts. From 26 of Sept 2003 up to 10 of March 2011: the area of future seismic catastrophe is characterized by relatively low α and the previous area of low α values is split into North and South parts. From March 14, 2011 by April, 2018: the North part of the relatively low α values before was realized as the area of Great Japan Earthquake 11 of March 2011, M=9.0, whereas the South part is still characterized by low α values.

13 α The sequence of mean maps of multi-fractal singularity spectra support width of lowfrequency seismic noise waveforms for 16 years of observations at broad-band seismic network F-net at Japan Small black star hypocenter of earthquake , M=8.3 Large black star hypocenter of Tohoku megaearthquake , M=

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15 Monthly maps of singularity spectrum support width α, corresponding to 2017

16 Monthly maps of singularity spectrum support width α, corresponding to 2018

17 Maps of low-frequency seismic noise wavelet-based normalized entropy, i.e. normalized entropy of the noise waveforms squared wavelet coefficients for the best orthogonal wavelet which is found for each station within each daily time window from the minimum of entropy En En N, deg N, deg E, deg E, deg From the beginning of 1997 up to 25 of Sept 2003: the area of future seismic catastrophe is characterized by relatively high values of normalized entropy and it is not split into North and South parts. From 26 of Sept 2003 up to 10 of March 2011: the area of future seismic catastrophe is characterized by relatively high values of normalized entropy and the previous area of high entropy values is split into North and South parts. From March 14, 2011 by April, 2018: the North part of the relatively high values of normalized entropy before was realized as the area of Great Japan Earthquake 11 of March 2011, M=9.0, whereas the South part is still characterized by high entropy values.

18 En The sequence of mean maps of minimum normalized entropy of squared orthogonal wavelet coefficients of low-frequency seismic noise waveforms for 16 years of observations at broad-band seismic network F-net at Japan. Small black star hypocenter of earthquake , M=8.3 Large black star hypocenter of Tohoku megaearthquake , M=

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20 Averaged monthly maps of minimum normalized entropy of squared wavelet coefficients, corresponding to 2017

21 Averaged monthly maps of minimum normalized entropy of squared wavelet coefficients, corresponding to 2018

22 Maps of low-frequency seismic noise waveforms kurtosis κ κ N, deg 45 N, deg E, deg E, deg From the beginning of 1997 up to 25 of Sept From 26 of Sept 2003 up to 10 of March From March 14, 2011 by April, Kurtosis = ( x) ( x) , x - increments after removing tidal trends within each daily window

23 κ The sequence of mean maps of seismic noise waveforms kurtosis for 16 years of observations at broad-band seismic network F-net at Japan. Small black star hypocenter of earthquake , M=8.3 Large black star hypocenter of Tohoku megaearthquake , M=

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25 Averaged monthly maps of seismic noise waveforms kurtosis, corresponding to 2017

26 Averaged monthly maps of seismic noise waveforms kurtosis, corresponding to 2018

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29 Averaged maps of annual correlation coefficient between multi-fractal singularity spectrum support width α and generalized Hurst exponent α* within moving time window of the length 5 days with mutual shift 7 days N, deg E, deg Before Tohoku EQ the region of future event was extracted by relatively high values of correlation coefficient What is observed for time period March 14, 2011 April, 2018

30 Averaged maps of annual correlation coefficient between kurtosis and generalized Hurst exponent within moving time window of the length 5 days with mutual shift 7 days N, deg N, deg E, deg E, deg Before Tohoku EQ the region of future event was extracted by relatively high values of correlation coefficient. What is observed for time period March 14, 2011 April, 2018

31 Averaged maps of annual correlation coefficient between minimum wavelet-based normalized entropy and generalized Hurst exponent within moving time window of the length 5 days with mutual shift 7 days Before Tohoku EQ the region of future event was extracted by relatively low values of correlation coefficient (high absolute values) What is observed for time period March 14, 2011 April, 2018

32 Cluster analysis of daily median values of 3 multifractal parameters within moving time window ( t ) * ( t) Let s consider moving time window of the length L =5 days and let ξ = ( α, α, α ) be 3D vector within current time window, t = 1,..., L, t is time ( t ) index, numerating vectors. Our purpose is investigating clustering properties of clouds of 3D vectors ξ with each 1-year time window. In particular, we are interesting what is the best number of clusters. Before making cluster procedure a preliminary operation of normalizing and winzorizing was performed within each time window for each scalar component ( t ) ( ) ξ k of vectors ξ t ( ). Here k = 1,2,3 is the index numerating scalar components of the vector ξ t 1 L ( t ) 2 1 L. Let ξk = L ξ, ( ( t ) 2 t= 1 k σ ) k = ( L 1) ξ t 1 k ξ be sample = ( t ) estimates of mean values and variance of scalar components of the 3D vector ξ. Let s perform iterations which consist in coming to values ( t ) ( t ) ( ) ζ k = ( ξ k ξk ) / σ k and clipping values ζ t k exceeding over and under thresholds ± 4σ k. These iterations are stopped when the values ξ k and σ became stable k and equal to the following values: ξ = k 0, σ 1. After this preliminary operation at each current time window we have a cloud consisting of L 3D vectors ( t) ζ. k = Let s split some cloud into given probe number q of clusters using standard k-means cluster procedure. Let Γ r, r = 1,..., q be clusters, zr ζ / n ζ Γ r mean r ( ) vector of the cluster Γ r, n r be a number of vectors ζ t q within cluster Γ r, n = L. K-means procedure minimizes sum with respect to positions of clusters centers z r. Let the best number of clusters r= 1 r S z z z z1,..., zq min q 2 ( 1,..., q) = ζ r min z1,..., zq r= 1 ζ Γ r J ( q) = min S( z,..., z ) * q was solved from maximum of pseudo-f-statistics: 1 q =. We try probe number of clusters within range 2 q. The problem of selecting PFS( q) = σ ( q) σ ( q) max, σ = J ( q) /( L q) q 0 σ ( q) ν z z, ν n / L, z ζ / L q L 2 2 ( t) 1 = r r 0 r = r 0 = r= 1 t= 1

33 Pseudo-F-statistics map as an estimate of current seismic danger (a1)-(a3) plots of daily median values of multifractal singularity spectrum support width α, * generalized Hurst exponent α and minimum Hölder-Lipschitz exponent α min from all 78 stations of broadband seismic network F-net in Japan, green lines present running average within time windows of the length 57 days; (b) plot of the best numbers of clusters for the sequence of clouds consisting of 5 daily 3D * vectors ( α, α, α min ) from moving time window of the length 5 days with mutual shift 3 days. The best number of clusters is defined from the maximum of pseudo-f-statistics. Vertical red line indicates time moment of Tohoku mega-earthquake on March 11, 2011, M = 9.1. Two-dimensional diagram (c) presents dependence of pseudo- F-statistics on the probe number of clusters which is varying from 2 up to within each time window. Plot (d) presents mean value of pseudo-f-statistics averaged by all probe numbers of clusters in dependence on right-hand end of moving time window of the length 5 days. Plot (e) presents the sequence of time moments of strong earthquakes M 7 in the rectangular domain with coordinates 28 ο N Latitude 48 ο N; 128 ο E Longitude 156 ο E which is a rather broad vicinity of Japan islands.

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35 Time Prediction: Squared correlation between mean values of multi-fractal parameters and of microseisms from all F-net stations estimated within 1 year moving time window. 0.8 Strong earthquake is only a foreshock , M = , M = Mean correlation since Best fitted line to the points of minimum correlations 0.4 Blue arrows were plotted at the April of The 2-nd arrow indicates the middle of 2010 as the lower estimate of catastrophe time moment Right-hand end of moving time window of the length 1 year

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37 Dangerous spots of high coherence of GPS noise at Nankai Trough Alexey Lyubushin, Institute of Physics of the Earth, Russian Academy of Sciences, Moscow, Network of 13 GPS stations in Japan Map of kernel estimate of probability density functions is presented for nodes of the regular net sized x, which are realized the spatial maximum of the frequency-dependent maximal values of the multiple spectral function of coherence of GPS time series from the nearest 10 workable stations for all 3 components of GPS time series with sampling time step 5 minutes within moving time window of the length 5 days. Time interval Maximum danger at the vicinity of the point N and 1E This result is an independent confirmation of the earlier conclusion made by the analysis of seismic noise Data were downloaded from the site of Nevada Geodetic Laboratory: ftp://gneiss.nbmg.unr.edu/rapids_5min/kenv/ E-005 1E-006 1E-007 1E-008 1E-009 1E-010 1E-011 1E-012 1E Periods, minutes Examples of multiple spectral coherence functions in dependence on periods from 10 nearest workable GPS station within time window of the length 5 days. Multiple coherence function: is based on using canonical coherences: The spectral matrices were calculated in a moving time window of the length 14 samples (5 days) with mutual shift 288 samples (1 day) for each node of regular grid from 10 nearest workable stations using model of vector autoregression of 5 th order. The GPS station is considered workable in the current time window if its registration interval includes the considered time window and the number of missing values does not exceed a predetermined maximum allowable proportion of the total length equal to 0.1. The missed values are filled using information about records from neighbor time interval of the same length as the length of gaps. Before calculating the spectral matrix in each time window the trend is removed by polynomial of 4 th order and ±3σ winsorizing was performed.

38 Conclusions. During preparing huge seismic catastrophe geological medium become more simple and homogenous. This manifests in the following properties of lowfrequency microseisms noise waveforms: 1) Multi-fractal singularity spectrum support width α decreases, i.e. the noise structure become more simple, less multi-fractal. 2) Minimum value of normalized entropy of squared orthogonal wavelet coefficients increases. 3) According to the results of seismic noise processing from Japan broadband network F-net the region of Nankai Trough could be the place of the next mega-earthquake. 4) In Japan, since 2003, the regime of natural fluctuations of seismic hazard has been established with a period of about 2 years. The previous 2-year cycle preceded the last strong earthquake Kumamoto on in the south of Japan. It follows that the next 2018 will be a year of increased seismic danger for Japan.