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An optimizing technique of the model parameters in the Region-Time-Length (RTL) algorithm

An optimizing technique of the model parameters in the Region-Time-Length (RTL) algorithm. Qinghua Huang Peking University, China. Contents. Introduction An improved RTL algorithm A case study of the 2011 M9.0 Tohoku earthquake. Background. Introduction.

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An optimizing technique of the model parameters in the Region-Time-Length (RTL) algorithm

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  1. An optimizing technique of the model parameters in the Region-Time-Length (RTL) algorithm Qinghua Huang Peking University, China

  2. Contents • Introduction • An improved RTL algorithm • A case study of the 2011 M9.0 Tohoku earthquake

  3. Background Introduction • Seismic quiescence has been paid much attention and tested in seismic risk evaluation (Mogi, 1979; Habermann, 1988; Wyss, 1991; ……) • Different attempts in revealing seismicity anomalies from seismological data (Wyss & Habermann, 1988 ; Kilis-Borok & Kossobokov, 1990; Wiemer & Wyss, 1994; Kossobokov et al., 1997; Wiemer, 2001; ……) • RTL algorithm is one of the effective methods of revealing seismicity changes (Sobolev & Tyupkin, 1997; Huang et al., 2001; Huang, 2006; ……)

  4. space time Place (x,y,z) Time (t) Tmax=2t0 past Rmax=2r0 ri ti t Epicenter of ith EQ Occurrence time of ith EQ RTL algorithm (Sobolev & Tyupkin, 1997) Introduction Basic assumption The weight coefficient of an event would be larger as this event is closer to the investigated place (x,y,z) or time (t) Huang et al., TECTO, 2001; Huang, JGR, 2006

  5. RTL algorithm (Sobolev & Tyupkin, 1997) Introduction The R(x,y,z,t), T(x,y,z,t), and L(x,y,z,t) are further normalized by their standard deviations, respectively  new dimensionless functions of R, T, and L RTL parameter is defined as the product of the above new R, T, and L. Huang et al., TECTO, 2001; Huang, JGR, 2006

  6. Case study: M8.0 Wenchuan EQ (May 12, 2008) Introduction Spatial distribution of seismic quiescence: Q-map Huang et al., GJI, 2002 Region under investigation RTL-curve at one position Quiescence map is obtained by changing the calculated position Q-map (2007.1-6) Huang, GRL, 2008

  7. Attempts in improving the RTL algorithm Introduction • Q-map: quantification of spatial distribution (Huang et al., GJI, 2002) • Stochastic test and correlation analysis of model parameters (Huang, JGR, 2006) • Improved correlation analysis of model parameters (Chen & Wu, GJI, 2006) • RTM algorithm (Nagao et al., EPS, 2011)

  8. Contents • Introduction • An improved RTL algorithm • A case study of the 2011 M9.0 Tohoku earthquake

  9. An optimal estimation of model parameters An improved RTL algorithm • Selection of model parameters is somehow empirical • Ambiguity in the RTL results arising from empirical parameter selection • Motivated by the empirical approach (Huang et al., 2001) and its improved version considering the correlation coefficients (Chen & Wu, 2006)  An improved technique of searching for the optimal model parameters

  10. The ratio/weight with correlation coefficients satisfying the given criteria An improved RTL algorithm Huang & Ding, BSSA, 2012 The optimal model parameters Cij: correlation coefficient I: logical function Number of r0: m Number of t0: n

  11. Test of the M7.3 Tottori EQ (Oct 6, 2000) An improved RTL algorithm r0: 25-100km, 2.5km, m=31 t0: 0.25-1.5yr, 0.05yr, n=26 C0=0.7 w0=60% (63.2km, 0.92yr) (50.0km, 1.00yr) empirical

  12. Test of the M8.0 Wenchuan EQ (May 12, 2008) An improved RTL algorithm r0: 25-100km, 2.5km, m=31 t0: 0.25-1.5yr, 0.05yr, n=26 C0=0.7 w0=60% (60.8km, 0.98yr) (50.0km, 1.00yr) empirical

  13. Contents • Introduction • An improved RTL algorithm • A case study of the 2011 M9.0 Tohoku earthquake

  14. A case study of Tohoku EQ EQ M=9.0, 2011.3.11 Investigated region 139-146˚E, 35–42˚N EQ data JMA,1985.01–2011.03

  15. A case study of Tohoku EQ Ideal case: lower Mc and longer catalog Mc=3.0, Jan 1, 1985 Data pre-processing Declustering(Molchan & Dmitrieva, 1992) Completeness analysis (Smirnov, 1998)

  16. A case study of Tohoku EQ • r0: 25-100km, 2.5km, m=31 • t0: 0.25-2yr, 0.05yr, n=36 • C0=0.7 • w0=60% • Optimized parameters (64.3km, 1.21yr)

  17. Temporal variation of the RTL parameter A case study of Tohoku EQ

  18. Spatial distribution of seismic quiescence A case study of Tohoku EQ

  19. Reliability analysis A case study of Tohoku EQ • Is the anomaly an artifact due to the selection of the model parameters? • Is it a frequently occurred anomaly?

  20. A case study of Tohoku EQ Stochastic test Calculated times:nanomaly:m=0 count: i=0 Generate a random catalog RTL curve Y N Anomaly? i<n? i=i+1 Y N m=m+1 Probability: P=m/n End

  21. Randomization of catalogs • generate a Poisson function with its mean value and time window the same as those of the real catalog • the time and epicenter satisfy uniform distribution • the magnitude satisfies an exponential distribution with a cutoff magnitude the same as that of the real catalog Result of stochastic test for 2000 randomized catalogs: P=0.016

  22. Summary • Proposed an improved technique of searching for the optimal model parameters in the RTL algorithm • Test the above technique and applied it to the analysis of seismic quiescence of the M9.0 Tohoku EQ

  23. The 8th International Workshop on Statistical Seismology http://geophy.pku.edu.cn/statsei8 Important dates Grant Application, 1st January ~ 28th February, 2013 Abstract Submission, 1st March ~ 31th May, 2013 Registration, 1st March ~ 31st May, 2013 Look forward to seeing you in Beijing!

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