SHARE-GEM hazard map released yesterday

PGA
475 years

D. Giardini, J. Woessner, L. Danciu, H. Crowley, F. Cotton, G. Gruenthal, R. Pinho, G.Valensise, S. Akkar, R. Arvidsson, R. Basili, T. Cameelbeck,
A. Campos-Costa, J. Douglas, M. B. Demircioglu, M. Erdik, J. Fonseca, B. Glavatovic, C. Lindholm, K. Makropoulos, F. Meletti, R. Musson, K.
Pitilakis, K. Sesetyan, D. Stromeyer, M. Stucchi, A. Rovida, Seismic Hazard Harmonization in Europe (SHARE): Online Data Resource, doi:
10.12686/SED-00000001-SHARE, 2013.
1

Selection and adjustment of empiricalground-motion. Results from the GEMShare European project

Fabrice	  Co,on
Ins0tut	
  des	
  Sciences	
  de	
  la	
  Terre,	  Université	
  Joseph	  Fourier,	
  Franceand	
  Disaster	
  Preven0on	
  Research	
  Ins0tute,	
  Kyoto	  University,	
  Japan	  Outline
Recent Projects
related to Groundmotion prediction in Europe-

Pegasos Refinement Project (Switzerland, SSHAC level 4
PSHA funded by Swissnuclear, 2002-2012)-

SHARE Project (New seismic hazard map of Europe
funded by the European Union, 2009-2012, European
regional component of GEM)
• Data access: www.efehr.org• Project information: www.share-eu.org

Goal	
  assigned	
  to	
  the	  SHARE	
  ground-­‐mo0on	
  working	
  group	  •Find	  the	
  smallest	
  set	  of	
  alterna0ve	
  models	  to	
  capture	
  the	
  full	  range	
  of	
  possible	
  ground-­‐mo0ons	  in	
  Europe•Consensus	  Outline
Outline• Context• Share	
  strategy	  • Key	  discussions	
  during	
  the	
  project• Latest	
  European	
  GMPE’s• Will	  future	
  Probabilis0c	
  Seismic	
  Hazard	
  Studies	
  
lead	
  to	
  higher	
  levels	  ?

6Various	
  tectonic	
  contexts

Basili, 2012 7

Various rock conditions• Shield : Vs30=2500 m/s• Continental Europe :Vs30=800m/s

Context : 100s of GMPEs

Courtesy of J. Douglas

Context : new networks but (hopefully) few strong-motion data Accelerometric data
 Mw=2.5-5.4
 Dist=15-200 km

Drouet10et al., 2010

Who	
  ?
• Several	
  Ins,tu,ons	
  (partners)

11	
  na%onali%es– METU,	
  Turkey	  	
  (S.	  Akkar)– UJF,	
  France	  	
  (PY	  Bard,	
  C.	
  Beauval,	
  F.	
  Co,on)– BRGM,	
  France	  (J.	  Douglas,	
  A.	
  Lemoine)	
  – AUTH,	
  Greece	  (K.	
  Pi0lakis)– ETHZ,	
  Switzerland	
  (D.	  Faeh,	  V.	
  Poggi,	
  B.	
  Edwards,	
  L.	
  Danciu	  )– KOERI,	
  Turkey	
  (M.	  Erdik)• A	
  core	  of	
  experts	
  (J.	  Bommer,	
  F.	
  Bonilla,	
  H.	
  Bungum,	
  E.	
  Faccioli,	
  F.	
  Scherbaum	
  N.	
  Theodulidis)• Several	
  postdoc	
  and	  PhD	  posi,ons	
  supported	
  by	
  the	
  project	
  S.	
  Drouet,	
  E.	
  Delavaud,	
  	
  M.	
  Segou,	
  E.	
  Yenier,	
  A.	
  Sandikayya,	
  E.	
  Riga	  • Coordina,on:	
  	
  F.	
  CoPon	
  and	  S.	
  AkkarOrganisa0on	
  inspired	
  from	  SSHAC	  guidelines

Courtesy	
  of	
  John	  Anderson

2/3	
  workshops	
  per	
  year	  to	
  favor	
  interac0ons	
  and	
  feedbacks

13

Selection of candidate GMPEs! Identification of worldwide GMPEs! Application of the exclusion criteria of Cotton et al. (2006)! Review of the GMPEs applicability range! Adjustment for parameter compatibility! Evaluation of the GMPEs using the criteria of Bommer et al. (2010)

Testing using data

Expert elicitation! Logic trees from 6 experts! Rankings of GMPEs basedon Scherbaum et al. (2009)

Proposition of logic trees : WP4 consensus! Selection of the final GMPEs! Proposition of different sets of weights

Sensitivity analysis of the proposed weights on the seismic hazard

Final logic tree

Delavaud, Cotton,
Akkar et al., 2012,
Journal of
Seismology

Exclusion criteria••

Applied	  these	  criteria	
  to	
  all	  models	
  in	
  compendium:1.

the	
  model	  is	
  from	  a	
  clearly	
  irrelevant	
  tectonic	  regime;2.

the	
  model	  is	
  not	
  published	
  in	
  an	
  interna,onal	
  peer-­‐reviewed	
  journal;

3.

the	
  documenta,on	
  of	
  model	  and	
  its	
  underlying	
  dataset	
  is	
  insuﬃcient;

4.

the	
  model	  has	  been	  superseded	
  by	
  more	  recent	
  publica,ons;

5.

the	
  frequency	
  range	  of	
  the	  model	  is	
  not	
  appropriate	
  for	
  engineering	
  applica,on;

6.

the	
  model	  has	  an	
  inappropriate	
  func,onal	
  form;	
  and

7.

the	
  regression	
  method	  or	
  regression	
  coeﬃcients	
  are	
  judged	
  to	
  be	
  inappropriate.

Criteria	
  1	
  means	
  GMPEs	
  only	  selected	
  for	  their	
  proposed	
  regime–

Uncertainty	
  in	
  regionalisa,on	
  handled	
  by	
  GEM	  WG	  on	
  this	  topic•

Criteria	
  2	
  was	
  occasionally	
  relaxed,	
  par0cularly	
  for	
  SCRs•Criteria	
  4	
  removes	
  the	  majority	
  of	
  pre-­‐2000	
  models•

Criteria	
  5	
  removes	
  all	  PGA-­‐only	
  models	
  +	
  GMPEs	
  only	  for	
  0.05<T<2s•

Criteria	
  6	
  and	  7	  remove	
  most	  local	
  models,	
  non-­‐parametric	
  models	
  and	
  those	  not	
  derived	
  using	  maximum-­‐likelihood/random-­‐eﬀects	
  techniquesSelection of candidate GMPEs! Identification of worldwide GMPEs! Application of the exclusion criteria of Cotton et al. (2006)! Review of the GMPEs applicability range! Adjustment for parameter compatibility! Evaluation of the GMPEs using the criteria of Bommer et al. (2010)

Testing using data

Expert elicitation! Logic trees from 6 experts! Rankings of GMPEs basedon Scherbaum et al. (2009)

Proposition of logic trees : WP4 consensus! Selection of the final GMPEs! Proposition of different sets of weights

Sensitivity analysis of the proposed weights on the seismic hazard

Final logic tree

Delavaud, Cotton,
Akkar et al., 2012,
Journal of Seismology

Greece	  -­‐	  	
  INSLAB	
  earthquakes
LLH	  versus	
  frequency	
  42

Atkinson&Boore	
  2003
McVerry	
  et	
  al.	
  2006	  Kanno	  et	
  al.	
  2006	
  Youngs	
  et	
  al.	  1997

41

40

Lin	
  and	  Lee	
  2008
BCHydro	
  2010Zhao	
  et	
  al.	
  2006

39

38

37

36

35
20

22

24

26

28

Best	
  ﬁkng	  :	
  	
   Zhao	
  et	
  al	
  [Japan]	
  	
  and	
  Lin	
  and	  Lee	  [Taiwan]
17

Overall	
  philosophy	
  of	
  experts• Aim	
   to	
   conserve	
   the	   minimum	
   number	
   of	
   models	
   that	
  allows	  capturing	
  of	
  epistemic	
  uncertainty• Aim	
   to	
   conserve	
   models	
   that	
   can	   be	
   applied	
   and	   weighted	
  similarly	
   for	   en0re	
   M,	   R	   and	   frequency	
   range	
   to	
   facilitate	  PSHA	  and	  to	
  avoid	
  strange	
  results	
  at	
  edges	
  of	
  bins• Aim	
   to	
   conserve	
   only	
   models	
  derived	
  using	
  state-­‐of-­‐the-­‐art	
  techniques• Retained	
   only	
   NGA,	
   pan-­‐European	
   and	   Japanese	   models	
  since	
   they	   are	   backed	
   by	
   large	
   datasets	
   and	   take	
   into	  account	  the	
  magnitude-­‐scaling	
  of	
  ground-­‐mo0ons	
  Delavaud, Cotton, Akkar et al.,2012, Journal of Seismology

Selection of candidate GMPEs! Identification of worldwide GMPEs! Application of the exclusion criteria of Cotton et al. (2006)! Review of the GMPEs applicability range! Adjustment for parameter compatibility! Evaluation of the GMPEs using the criteria of Bommer et al. (2010)

Testing using data

Expert elicitation! Logic trees from 6 experts! Rankings of GMPEs basedon Scherbaum et al. (2009)

Proposition of logic trees : WP4 consensus! Selection of the final GMPEs! Proposition of different sets of weights

Sensitivity analysis of the proposed weights on the seismic hazard

Final logic tree

Toward	
  a	
  consensus	  ?
• Models	
  selected	
  by	
  the	
  data-­‐tes0ng	
  AND	  all	
  experts	  have	  been	  selected• Models	
  selected	
  either	  by	
  some	  of	
  the	
  experts	
  or	
  the	
  data-­‐tes0ng	
  have	
  been	  discussed.
• A	  “consensus”	
  logic	
  tree	  has	  been	
  chosen• Sensi0vity	
  tests	
  have	  been	  performed	
  to	
  evaluate	
  the	
  diﬀerence	
  between	  the	  resul0ng	
  ”common”	  	
  logic	
  tree	  and	  individual	
  choices	
  :	
  selec0on	
  is	
  more	  important	
  than	  weigh0ng•

Table 9 Expert choices, data-based testing results and logic

Subduc0on	
  Regionstrees for subduction zones. Names in bold are the selectedmodels. The preferred weighting scheme (WS) is shown inbold.
Category based on expert judgment

Models supported by all the experts
Models supported by a majority of experts
Category based on data-testing results

Models supported by the testing
for long periods (T > 0.16s)
Models supported by the testingfor short periods (T  0.16s)

Models
Lin and Lee (2008)
Zhao et al. (2006)

Atkinson and Boore (2003)
Zhao et al. (2006)

Category based on expert judgment
and data-testing results

ModelsModels supported by the data testing
and the experts choices
Models chosen by a majorityof experts and supportedby the data-testing results
Models chosen by a minorityof experts or with a low
data-testing result
Models not supported by the data-testingand not chosen by the experts

Selected Models
Zhao et al. (2006)
Atkinson and Boore (2003)
Youngs et al. (1997)
Lin and Lee (2008)

ModelsAtkinson and Boore (2003)
Youngs et al. (1997)
Zhao et al. (2006)

WS1
0.4
0.2
0.2
0.2

Zhao et al. (2006)

McVerry et al. (2006)
Atkinson and Macias (2009)
Garcia et al. (2005)

WS2

0.25
0.25
0.25
0.25

Delavaud, Cotton, Akkar et al., 2012,
Journal of Seismology
21

Stable	  Con0nental	
  Regions

Toward a Ground-Motion Logic Tree for Probabilistic Seismic Hazard Assessment in Europ
Table 8 Expert choices and the final logic trees for stable

continental regions. Names in bold are the selected models.
WS stands for weighting scheme.
Category based on expert judgment

Models supported by all the experts
Models chosen by a majority of experts

Models chosen by a minority of experts
Models not chosen by the experts

Selected models for shield
Campbell (2003)
Toro (2002)

ModelsCampbell (2003)
Toro et al. (1997)

Atkinson and Boore (2006)
Douglas et al. (2006)
Atkinson (2008)
Tavakoli and Pezeshk (2005)

WS

0.5
0.5

Selected models for continental crustCampbell (2003) adjusted to 800m/s
Toro (2002) adjusted to 800m/s
Akkar and Bommer (2010)
Cauzzi and Faccioli (2008)
Chiou and Youngs (2008)

WS

0.2
0.2
0.2
0.2
0.2

Delavaud, Cotton, Akkar et al., 2012,
Journal of Seismology
22

"$%&'()) *+,,-. $/0123(/ 34 5,,%62,F+

G<$4 !" #$% *+,,H.

7484 *+,,+9 1:&1');2<(/. $/0123(/ 34 5,,%62 ,F+!"#=>>$8 $:/ ?4%%(8 *+,@,.

,F+"$1AA; $:/ B$CC;4); *+,,5.

,F+"<;41 $:/ D41:E2 *+,,5.

,F+

,FL

=3>;:24: $:/ ?448( *+,,-. ,F+!G

D41:E2 !" #$% *@IIJ.

,F+

K;: $:/ K(( *+,,5.

,F+=>>$8 $:/ ?4%%(8 *+,@,. ,F-M=!"#
7 ≤ -2"$1AA; $:/ B$CC;4); *+,,5. ,F-M
G<$4 !" #$% *+,,H.

,F@"<;41 $:/ D41:E2 *+,,5.

,F+

Ocenanic crust: same logic tree as ASCR=!"#
-2 < 7 ≤ @,2

N4)C$:;C A4:(2"$1AA; $:/ B$CC;4); *+,,5. ,FM"<;41 $:/ D41:E2 *+,,5.

,FM

B$CC;4); !" #$% *+,@,.

@

Vrancea: same logic tree as SZ

Figure 3. Ground-motion logic trees for the Euro-Mediterranean region.
Delavaud, Cotton, Akkar et al., 2012, Journal of Seismology

Key	
  discussions	  during	
  
t
he	
  
p
rojectOutline– Global	
  or	
  local	
  models	  ?
– What	  is	  a	
  rock	  site	  ?
– Beyond	  GMPE’s	  ?
– Removing	  the	
  ergodic	
  assump0on	
  ?
– Regionalisa0on	
  of	
  GMPE’s– Vs30/slope	
  correla0ons

How to use local weak-motion data ?

 Accelerometric data
 Mw=2.5-5.4
 Dist=15-200 km

25

What	  do	
  regional	
  data	  show	
  us	
  ?	
  

Assumptions:Far-field approximation (Dist>15 km)
Brune’s type source (1970)
Average radiation patternvS constant along the path
Geometrical decay constant between 15 and 200 km
Q(f)=Q0fα

SourcePropagation

Site

Drouet et al., 2008, 2010

Stress-­‐drop	
  regional	
  varia0ons	
  in	
  France

Red: Alps
Green: Rhine Grabben
Blue: Pyrenees

Drouet et al., 2008, 201027

Question (unresolved ) : are stress- drop magnitude-dependent ?
Regional stochastic GMPEs for France

Courtesy of S. Drouet, 2013

Figure 4 – Left : Stress drop versus moment magnitudes for three regions of the French
metropolitan area (Alps: red crosses; Pyrenees: blue crosses; Rhine Graben:green crosses) and for the French West Indies (grey crosses) ; average
moment magnitudes and stress drops for each data sets are also plotted(circles). Data from the NGA flatfile (http://peer.berkeley.edu/nga/ last accessed
March 2012) are also plotted (black crosses). The solid lines represent theregional stress drops models used in the simulations. Right : comparison of teh
stress drop model for the Alpes and the models for Switzerland (Edwards, 2012,Unresolved question : reproducing large earthquake groundmotion decay given small magnitude earthquake decay

29
Akkar and Bommer, 2007

The site part is the most useful information(on the short term)
Assumptions:Far-field approximation (Dist>15 km)
Brune’s type source (1970)
Average radiation patternvS constant along the path
Geometrical decay constant between 15 and 200 km
Q(f)=Q0fα

SourcePropagation

Site

Drouet et al., 2008, 2010

What is a rock site ?
• Shield : Vs30=2500 m/s• Continental Europe : Vs30=800m/s

Site part : kappa ?

Boore & Joyner (1997)

Kappa dependencies on Vs30 :« rock » site are heterogeneous !

Van Houtte et al., BSSA, 2011

Kappa dependencies on Vs30 :Kappa dependencies on Vs30 :« rock » site are heterogeneous !
« rock » site are heterogeneous !

RockHardrockVan Houtte et al., 2011

	
  GMPEs	
  weaknesses	
  :	
  complexity,	
  lack	  of	
  physic	
  bases,	  trade-­‐oﬀ	
  between	
  site/path/source	
  coeﬃcients	
  	
  ?	
  

Beyond	
  GMPE’s	  ?
Towards simulations ? Need for a probabilistic
description of future earthquakes rupture properties

Towards fully data-driven models (neural networks) ?
Need for large databases

Avoiding the specification of any a priori functional form, artificial neural
1450
B. Derras, P.-Y. Ba
networks (ANNs) provide fully data-driven predictive models

Analytical Description o

One of the reasons for th
neering practice is their simpli
complicated, results from AN
simple analytical tools.
For any given set of inpu
malization preprocessing accoPn . Log10 "PGA# is then obtainto equation (6). The scaled ou
equation (A3).

T n ! tanh$fb2 g % $w2 & × tan

Figure 3.

Architecture of the ANNs model for PGA prediction
represents the synaptic weights between the i-th neuron of the input
layer and the j-th neuron in the hidden layer, the bias of the j-thneuron in the hidden layer, wojk the synaptic weights between thej-th neuron of the hiddenbok layer and the k-th neuron in the output
layer, and the bias of the k-th neuron in the hidden layer.

where w1 is the "N h ; N# matr
the input parameters and thesize N h that contains the synden layer and the single-outpu
the bias vectors of the hidden
tively. The values of $w1 &, $w
Figure A1. The Tanh depende
38
the Tanh-sigmoid
activation f

1450

The ANN approach was inspired byinvestigations into the structure of the humanbrain
B. Derras, P.-Y. Bard, F.
An ANN is made up of interconnecting
artificial neurons, with the input and thehidden and output layers
Analytical Description of the A
The synaptic weights and bias of an ANN areof the reasonsfor the larg
obtained by learning, One
throughthe training
practiceis theirsimplicity.
dataset, to minimizeneering
an errorfunctionequal
to A
complicated,
results
frominANN
the sum of the squared
residuals(MSE)
the s cansimple analytical tools.
output layer
For any given set of input data
malization preprocessing according
Pn . Log10 "PGA# is then obtained thto equation (6). The scaled output T
equation (A3).

T n ! tanh$fb2 g % $w2 & × tanh"fb1

Figure 3.

Architecture of the ANNs model for PGA prediction
represents the synaptic weights between the i-th neuron of the input

where w1 is the "N h ; N# matrix of s
the input parameters and the hidden
size N h that contains 39
the synaptic w
den layer and the single-output para

Neural Network Approach to PGA Prediction: An Example Based on the KiK-net Data

1449

KiK-net seismic database(3891 records from 398 sites
and 335 earthquakes

Derras et al.
2012, BSSA
40

Kanno et al. (2006) used only two parameters, M
source distance,
the closest
from the fau
Although not imposed a priori, ANN results
have aasnumber
ofdistancephysically
GA prediction
equation (equation
6) was also comand they
selected
data frothe observationsite Rrupsound features:clear magnitude
and depth
dependencyof, thedecayof thethreeother recent
attenuation
relations derived
fortabasesaturationfrom KiK-net
K-NET
for the regression
groundmotionwith distanceand near-fault
forandlarge
magnitudes,
se region. These are the GMPEs proposed by Zhao
Model d, was derived from the same Kik-net
6), Kanno et al. (2006), and Cotton et al. (2008),
The GMPE function was described by the following

parison of the ANN Model with Other Studies

41

From these coefficients Pi , the most importsome extent be the result of the arbitrarily chosen
the epicentral distance, as log10 "R# (29.53
de and depth bins, although Testing
there wereofno
theclear,relativeis importancedirectlyby the earthquake
magnitude, M w (2nt trends. However,this apparent
increase in variables
theof the effects
of independent
on seismic
ground motion

sigma

Adapting the Neural Network Approach to PGA Prediction: An Example Based on the KiK-n

6.

Evolution of the overall fit of the ANN model (as measured with sigma), according
to willthe benumberandwhichreferredto scalinas mors considered.
(equation 6 is model a). Model

Iranian, and western U.S. daequation:

ln!PGA" # 1:101 × Mw − 0− ln!Rrup $ 0:00

$ 0:01412 × !HFigure 10.

Bars represent the sensitivity of the input variables
observed during the modeling.

where PGA is in Gal; Mw , Rru
moment magnitude, the closes
42 respectively;
the focal depth,
ciated with a specific site classDepth is shown to affect moderate magnitude
events ground-motions in the epicentral area

B. Derras, P.-Y. Bard, F. Cotton, and A. Bekk

12.

Graph shows the relationship between PGA and epicentral distance, and the influence of the focal depth on the pre
43
the near field (V S30 # 600 m=s and f0 # 5 Hz).

Table 2; sigma given by the ANN model is slightly lowe
ment magnitude and the closest distance to the rupcompared
the other threeclassicfor
models.
These cctively.The results show a clear increase in PGA
withwithdecreasing
VS30
given
sameclassicalpotential as trad
showcorrespondsthat ANN has the
PGA attenuation
curves forand isons
15 values
shows theof magnitude,
distances,
f0. Thisto theGMPEs to predict the attenuation of the seismic wav
c, andd as a function
the rupture
distance
observation
thatofstiff-soil
siteshave lower
acceleration than softer sites, atdistance and the specificity of the earthquakes of the Jar model a as a function of the hypocentral distance,
least at low accelerationlevelsagnitudes of 5, 6, for depth equal to 10 km, and
area.

44

Graph shows the relationship between PGA and epicentral distance, and the influence of the site condition parameter

Comparison with three other recent attenuation relations derived for
the Japanese region.

Comparisons of the predicted PGA for the present model with those of Zhao et al. (2006), Kanno et 45
al. (2006), andwith V S30 ! 600 m=s, f0 ! 5 Hz, and for M w 5 and 6. The ANN model uses the epicentral distance; the other models

Special issue : Bull. Earth. Engineering
A new generation of ground-motion models for
Europe and the Middle East
The special issue begins with a paper of Akkar et al. on the common
strong-motion database used to derive all ground-motion models
published in the special issue.
Two of the groups (Sandıkkaya & Akkar ; Massa et al.) use random-effects
regression analysis.
Kuehn et al., Bora et al. and Derras et al. develop their models using novel
techniques (not only SA but also Ia, SMD, CAV).
Akkar et al. extend the model developed by Sandıkkaya & Akkar, for theprediction of the horizontal component of earthquake response spectra
for 5% of critical damping, to other damping levels and verticalcomponent of motion.
The special issue concludes with a comparison of the response spectra
predicted by the different models for various earthquake scenarios by
Douglas et al.
46
Deadline : end of November 2012
Publication : autumn 2013

European strong-motion database
Bulletin of Earthquake Engineering, special issue

47

Derras et al, 2013

Ground-motion predictions : Vs=760m/s, Rjb=10km

Texte

48

Douglas et al., 2013

Share results from a GMPE point of view
• An «homogeneous» European strong-motion databank• GMPE’s selection and weighting using both data-testing andexpert opinions• Regionalisation of Europe for the use of GMPEs• Toward a better rock site definition (Vs30 and kappa,Vsprofile and kappa)• Toward new ideas on GMPE’s (Rhypo/Rrup,Neural networks)

Will future Probabilistic Seismic Hazard
Studies lead to higher levels ?
• The epistemic uncertainty of seismicity models may be larger
than what we thought...
• But, ground-motion models may be better calibrated :– site and source specific simulations– use of site monitoring to calibrate the site-specificamplification and non-ergotic (single-station) aleatory
variability– source and path dependency of the aleatory variability•

Will future Probabilistic Seismic Hazard
Studies lead to higher levels ?
• The epistemic uncertainty of seismicity models may be larger
than what we thought (Mmax increases)• But...

Slight	
  changes	
  of	
  the	
  aleatory	
  variability	
  have	  a	
  large	  impact	
  on	
  PSHA

Increasing σ

Bommer and Abrahamson (2006)

The	
  observed	
  variability	
  has	  not	
  decreased	
  these	  last	  30	
  years..
~3000	
  records~300	
  recordsYear	
  σ[ln(Y)]	
  =	
  2.3σ[log10(y)]Strasser	
  et	
  al.	  (2009)

0.7	
  ln	
  scale	  0.3	
  log	  sc	
  aleSingle station sigma : the ground-motion variability due tosite-to-site variability is moved from the aleatory to theepistemic part

SS

Site	
  to	
  site	  variability	
  variability	
  within	
  a 	
  
site	
  class	  (variability	
  of	
  site	  proper0es)	
  need enough records toevaluate the site specificamplification factor !
For site specific-studies
only (and not PSHA maps)

Single-station within-event variability : 1000 stations (for which
the site term has been evaluated from a minimum of 5 records)
0.8
0.7

I ss

0.6
0.5
0.4
0.3
0.1
0.2

Singlestation
0.5
0.2 0.3

3.0

1.0

0.2 0.3 0.5
0.1
Period (Sec)

PGA

1.0

3.0

1.0

3.0

Period (Sec)

0.8
0.7

California
Swiss
California
TaiwanSwiss
TaiwanTurkeyTurkeyJapanJapan
All Data
All Data

I

0.6
0.5
0.4

Ergodic

0.3
0.2

PGA

0.1

0.2 0.3 0.5

Period (Sec)Rodriguez-Marek et al., 2013

Use of generic (non-region dependent) single-station sigma
model within the Pegasos project
0.8

Magic value of 0.45 ?

0.7

I ss

0.6
0.5
0.4
0.3
0.1
0.2

Singlestation
0.5
0.2 0.3

3.0

1.0

0.2 0.3 0.5
0.1
Period (Sec)

PGA

1.0

3.0

1.0

3.0

Period (Sec)

0.8
0.7

California
Swiss
California
TaiwanSwiss
TaiwanTurkeyTurkeyJapanJapan
All Data
All Data

I

0.6
0.5
0.4

Ergodic

0.3
0.2

PGA

0.1

0.2 0.3 0.5

Period (Sec)Rodriguez-Marek et al., 2013

Will future Probabilistic Seismic Hazard
Studies lead to higher levels ?
• The epistemic uncertainty of seismicity models may be larger
than what we thought...
• But, ground-motion models may be better calibrated :– site and source specific simulations– use of site monitoring to calibrate the site-specificamplification and non-ergotic (single-station) aleatory
variability– source and path dependency of the aleatory variability•

SHARE hazard map released yesterday

D. Giardini, J. Woessner, L. Danciu, H. Crowley, F. Cotton, G. Gruenthal, R. Pinho, G.Valensise, S. Akkar, R. Arvidsson, R. Basili, T. Cameelbeck,
A. Campos-Costa, J. Douglas, M. B. Demircioglu, M. Erdik, J. Fonseca, B. Glavatovic, C. Lindholm, K. Makropoulos, F. Meletti, R. Musson, K.
Pitilakis, K. Sesetyan, D. Stromeyer, M. Stucchi, A. Rovida, Seismic Hazard Harmonization in Europe (SHARE): Online Data Resource, doi:
10.12686/SED-00000001-SHARE, 2013.
58