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Ground motion prediction and intensity conversion relations for the European region. Mathilde B. Sørensen and the SAFER WP4 team. Source parameters. Data. Real time shake maps. Site effects. PGx vs. I relations. Ground motion prediction equations. Work done. Iceland:

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ground motion prediction and intensity conversion relations for the european region

Ground motion prediction and intensity conversion relations for the European region

Mathilde B. Sørensen and the SAFER WP4 team

slide2

Source parameters

Data

Real time shake maps

Site effects

PGx vs. I relations

Ground motion prediction equations

slide3

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)

slide4

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
slide6

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

slide8

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

slide9

Vrancea region - Intensity prediction relation

Epicentral distance

Rupture distance

J-B distance

Similar regression error (~0.6) for the three distance measures

Partner: GFZ

slide10

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

slide12

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

slide15

PGV values from 46 earthquakes

PGA values from 46 earthquakes

Ground motion prediction equations for southwest Iceland (D5.2)

Partner: IMOR

slide16

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

slide17

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

slide19

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

slide20

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

slide21

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

slide22

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

slide23

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

slide25

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

slide26

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

slide27

Campania region - Results

  • Variation in regression paramteres compensated
  • Similar regression error indicating uncertainty in intensity data

Partner: GFZ

slide28

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

slide29

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

slide31

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

slide32

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

slide33

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

slide34

Italy – PGx vs. I

PGV

PGA

Partner: INGV

slide35

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

slide46

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:

slide47

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:
slide50

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

slide52

Vrancea region – previous relations

Various zonatins or lacking information makes direct comparison to previous relations difficult

Advantages of our relation: Physical basis and easy implementation

slide54

Marmara Sea region - Data

Özmen, 2000

  • Digitized isoseismial maps from 7 earthquakes
  • 1999 event from Özmen (2000)
  • Remaining from Eyidogan et al. (1991)
  • Source parameters taken from various publications, mostly on the indvidual events
slide55

Vrancea region - Data

Bonjer, pers. comm., 2007

  • Digitized intensity maps from 5 important Vrancea earthquakes
  • Intensity maps of University of Karlsruhe
  • Anisotropic intensity distribution
  • Source parameters taken from various publications, mostly on the indvidual events
slide56

Vrancea region - Results

  • Regression error
  • Close to error in a new intensity estimate using the relation
  • Similar error for the three distance measures
slide57

PGM vs I relations - Results

Marmara Sea

Vrancea

Campania

Weighted regression seems to represent average relation

Other relations are within the 68% prediction bounds of the weighted relation

The spread indicates the level of uncertainty due to few data with large spread

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