Ground motion prediction and intensity conversion relations for the European region

1. Ground motion prediction and intensity conversion relations for the European region Mathilde B. Sørensen and the SAFER WP4 team

2. Source parameters Data Real time shake maps Site effects PGx vs. I relations Ground motion prediction equations

3. Work done Iceland: Ground motion prediction relations (WP5 – IMOR) PGx vs. I (IMOR) Bucharest: Intensity prediction relation (GFZ) PGx vs. I (GFZ) Naples: Intensity prediction relation (GFZ) Ground motion prediction relations (AMRA) PGx vs. I (INGV) Istanbul: Intensity prediction relation (GFZ) PGx vs. I (GFZ) PGx vs. I (KOERI) Athens: Ground motion prediction relations (NKUA) Cairo: Intensity prediction relation (GFZ, NRIAG)

4. Outline • Athens • Ground motion prediction • Bucharest • Intensity prediction • PGx vs. I • Cairo • Intensity prediction • Iceland • Ground motion prediction • PGx vs. I • Istanbul • Intensity prediction • PGx vs. I • Naples • Intensity prediction • Ground motion prediction • PGx vs. I • Comparison

5. Athens

6. Athens Two step stratified regression technique (Fukushima and Tanaka 1990, Joyner and Boore 1981) was used in order to derive site depended attenuation relationships. • log10 (PGA) = 0.65Μ -1.61 log10Χ + 0.20S + 0.71 • log10 (PGV) = 0.83Μ -1.44 log10Χ + 0.15S – 1.74 • log10 (PSA0.1) = 0.55Μ -1.57 log10Χ + 0.13S + 1.24 • log10 (PSA0.2) = 0.59Μ -1.23 log10Χ + 0.15S + 0.54 • log10 (PSA0.5) = 1.16Μ -2.12log10Χ + 0.16S - 0.54 • log10 (PSA1) = 1.14Μ -1.63log10Χ + 0.23S - 1.74 • log10 (PSA2) = 1.2Μ -1.46log10Χ + 0.21S - 2.88 • log10 (PSA3) = 1.15Μ -1.37log10Χ + 0.23S - 3.25 Selected 397 records from 73 events recorded to more than 4 stations Partner: NKUA

7. Bucharest

8. Based on digitized intensity maps from 5 large Vrancea earthquakes Account for anisotropy by introducing spatial site correction function where dI is a site correction function Vrancea region – Intensity prediction relation Derive relation for three distance measures Partner: GFZ

9. Vrancea region - Intensity prediction relation Epicentral distance Rupture distance J-B distance Similar regression error (~0.6) for the three distance measures Partner: GFZ

10. Vrancea region – PGx vs. I Strong motion data from 1977 (ESD), 1986, 1990a, 1990b (NIEP-Uni Karlsruhe) events Test four weighting schemes (unweighted, weighted, log(mean(PGM)), mean(log(PGM))) Partner: GFZ

11. Cairo

12. Cairo – Intensity prediction • Based on intensity point data from 7 earthquakes • Unified magnitudes using empirical relations Mw(Ms) • Mean regression error: =0.58 • Compare to digitized isoseismal lines from the 1992 Cairo earthquake (not included in regression) Partner: GFZ, NRIAG

13. Iceland

14. 46 earthquakes in southwest Iceland used derive attenuation relations (D5.2) Partner: IMOR

15. PGV values from 46 earthquakes PGA values from 46 earthquakes Ground motion prediction equations for southwest Iceland (D5.2) Partner: IMOR

16. Attenuation models for a M6.5 event (D5.2) Thick line: PGA attenuation model developed in SAFER, based on velocity records from the national seismic network, SIL. Red dashed: (Olafsson and Sigurbjörnsson) theoretical model derived for Iceland Solid fucshia (Halldorsson and Sveinsson) based on strong motion data from 6 earthquakes in Iceland. The data poinst are PGA values obtained for the M6.5 June 17 2000 earthquake in the South Iceland Seismic Zone. Partner: IMOR

17. PGx vs. I relations based on 5 earthquakes in SW Iceland MMI = 1.9 log10(PGV) + 7.7MMI = 1.6 log10(PGA) + 5.7 Partner: IMOR

18. Istanbul

19. Marmara Sea region – Intensity prediction relation • Based on data from digitized isoseismial maps from 7 earthquakes • Mean regression error: =0.672 1912 1953 1999 Partner: GFZ

20. Marmara Sea region – PGx vs. I Strong motion data from 1983 Biga and 1999 Izmit earthquake from the European Strong-Motion Database (ESD) Test four weighting schemes (unweighted, weighted, log(mean(PGM)), mean(log(PGM))) Partner: GFZ

21. Marmara Sea region – PGx vs. I Derive PGx vs. I relation based on Observed intensities from 58 earthquakes Estimated PGA or PGV from ground motion prediction equations of Akkar and Bommer, Boore and Atkinson, Campbell and Bozorgniaand Gülkan and Kalkan Iakkar=4.9 * log(PGV) + 0.02 σ=0.8 Iboore =4.35 * log(PGV) + 1.57 σ=0.7 Icampbell=4.24 * log(PGV) + 1.96 σ=0.7 Ikalkan=6.6 * log(PGA) – 7.28 σ=0.9 Iboore=4.5 * log(PGA) – 2.73 σ=0.8 Icampbell=4.8 * log(PGA) – 2.98 σ=0.7 Partner: KOERI

22. Marmara Sea region – PGx vs. I The PGV-Intensity relationships obtained by Boore and Atkinson (2006) and Campbell and Bozorgnia (2006) attenuation relationships agree well with the Wald et al. (1999)’s PGV-Intensity relationship for I<6, The PGV-Intensity relationship obtained by Akkar and Bommer (2006) attenuation relationship is considerably different than Wald et al. (1999)’s PGV Intensity relationship for I<8. All PGA-Intensity relationships obtained in this study are considerably different than Wald et al. (1999)’s PGA-Intensity relationship. Partner: KOERI

23. Marmara Sea region – PGx vs. I Using the PGA/PGV distributions obtained for the Kocaeli earthquake with Boore and Atkinson (2006) attenuation relationships, intensity distribution for the Kocaeli earthquake was obtained as in the following figure. Partner: KOERI

24. Naples

25. Campania region - Data • Intensity points for 9 earthquakes from DBMI04 online database • Source parameters taken mainly from Gasperini et al. (1999) • Associate the source parameters with uncertainty Partner: GFZ

26. Campania region – Intensity prediction relation, Monte Carlo approach Intensity points for 9 earthquakes from DBMI04 online database Associate the source parameters with uncertainty Perform 1 Mio. regressions sampling source parameters within the given uncertainty bounds Compare to result of standard regression Partner: GFZ

27. Campania region - Results • Variation in regression paramteres compensated • Similar regression error indicating uncertainty in intensity data Partner: GFZ

28. Campania region - Results Effect of propagating uncertainties through Monte Carlo approach is negligible This indicates that the effect of uncertainties in source parameters is negligible in comparison to the spread in the intensity data This implies that intensity data for Italy can be predicted only within 1 intensity unit Partner: GFZ

29. Attenuation relationships for PGA and PGV in southern Apennines The dataset used to retrieve the attenuation relationship consisted of an integrated observed and synthetic strong-motion database that was obtained using the stochastic approach proposed by Boore (1983). The input parameters for the simulation technique, i.e., the average static stress-drop values and attenuation parameters (geometric and anelastic), were obtained through spectral analysis of waveforms from earthquakes recorded by the Istituto Nazionale di Geofisica e Vulcanologia (INGV) seismic network for a magnitude range Md (1.5, 5.0) over the last 15 years. Partner: AMRA

30. Attenuation relationships for PGA and PGV in southern Apennines Partner: AMRA

31. PGA PGV Attenuation relationships for PGA and PGV in southern Apennines Synthetic databases for PGA and PGV as functions of the epicentral distances for M 5, 6 and 7. Crosses refer to the data of November 1980/18:34 M 6.9 Irpinia earthquake 01 December 1980/19:04 M 4.6 aftershock 16 January 1981/00:37 M 4.7 aftershock Continuous black lines refer to the local attenuation relationships retrieved in this project while dotted and bold dashed lines refer respectively to the SP96 and CA97 attenuation relationships. Partner: AMRA

32. Italy – PGx vs. I Data: The Italian strong motion database,ITACA (Luzi et al., 2008) The Macroseismic Database of Italy, DBMI08 (Stucchi et al., 2007) • 266 PGM-IMCS data pairs (three times larger than those adopted previously for Italy; time period 1976-2004) Earthquakes PGM-IMCS pairs (From Faenza and Michelini, in publication) Partner: INGV

33. Italy – PGx vs. I Relation obtained through Orthogonal Distance Regression (ODR) allowing for • Inclusion of uncertainties for both independent and dependent variables • Direct inversion between PGM and I The regression has been applied to a binned data set, using the geometric mean Single-line regression is sufficient to fit the data • PGV single-line regression for IMCS ≥ VI IMCS = 5.11 ± 0.07 + 2.35 ± 0.09 log PGV • PGA single-line regression for IMCS ≤ VI IMCS = 1.68 ± 0.22 + 2.58 ± 0.14 log PGA Partner: INGV

34. Italy – PGx vs. I PGV PGA Partner: INGV

35. Example: MW6.3, April 6, 2009, L’Aquila main shock in Central Italy Instrumental Data ShakeMap Macroseismic Data Shakemap Preliminary Macroseismic Field Courteously from QUEST • - Good match between predicted and reported macroseismic data • - The regressions can be used to predict realistic ground motions from intensity data alone Partner: INGV

36. Comparison

37. Comparison of relations – intensity prediction M=6 M=7

38. Comparison of relations – ground motion prediction M=6 M=7

39. Comparison of relations – PGx vs. I

40. Comparison of relations – PGx vs. I

41. Comparison of relations – PGx vs. I

46. Regression model • Physically based ground motion prediction equations • Adjusted relations to fit the characteristics of the given region (important in early warning applications and for „special“ regions) • Easy to implement for the user Geometrical spreading Energy absorption Epicentral intensity (I0) Apply weighting scheme so each intensity class enters with the same weight Solve weighted least squares problem:

47. Three distance measures • Epicentral distance (distance to the epicenter, on the surface) • Joyner-Boore distance (shortest distance to surface projection of fault) • Rupture distance (shortest distance to the fault) • For rupture distance the functional relation must be updated:

48. Marmara Sea region - Results 1912 1953 1999

50. The uncertainties in the estimated parameters x and in predicting a new intensity I for given predictor values Mw, R, and h are connected with the covariance matrix C of the parameter estimates with and the mean squared regression error where m is the dimension of x (the number of model parameters). For a specified level of certainty α, the confidence bounds xc for the fitted parameters x are given by where t-1(p,ν) is the inverse of the cumulative t-distribution for the corresponding probability p and ν degrees of freedom. For ν ≥40, t-1(p,ν) ≈ N-1(p), the inverse of the cumulative standard normal distribution at p. In this case a certainty level of 68.3% (α=0.683) corresponds to the standard deviation (1σ) of normally distributed errors. Much more interesting in this study is the error of a new intensity prediction I of the estimated model. For given predictor values Mw, R, and h, this can be expressed by where y is the Jacobian of I-Ax at the predictor values: Uncertainties