SHARE-GEM hazard map released yesterday

Transcription

1 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: /SED SHARE,

2 Selection and adjustment of empirical ground-motion. Results from the GEM- Share European project Fabrice Co,on Ins0tut des Sciences de la Terre, Université Joseph Fourier, France and Disaster Preven0on Research Ins0tute, Kyoto University, Japan

3 Recent Projects Outline related to Groundmotion prediction in Europe - Pegasos Refinement Project (Switzerland, SSHAC level 4 PSHA funded by Swissnuclear, ) - SHARE Project (New seismic hazard map of Europe funded by the European Union, , European regional component of GEM) Data access: Project information:

4 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

5 Outline Context Share strategy Key discussions during the project Latest European GMPE s Will future Probabilis0c Seismic Hazard Studies lead to higher levels?

6 6

7 Various tectonic contexts Basili,

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

9 Courtesy of J. Douglas Context : 100s of GMPEs

10 Context : new networks but (hopefully) few strong-motion data Accelerometric data M w = Dist= km 10 Drouet et al., 2010

11 Who? Several Ins,tu,ons (partners) 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) 11 na%onali%es 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

12 Organisa0on inspired from SSHAC guidelines Courtesy of John Anderson

13 2/3 workshops per year to favor interac0ons and feedbacks 13

14 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) Expert elicitation Logic trees from 6 experts Testing using data Rankings of GMPEs based on Scherbaum et al. (2009) Delavaud, Cotton, Akkar et al., 2012, Journal of Seismology 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

15 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 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

16 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) Expert elicitation Logic trees from 6 experts Testing using data Rankings of GMPEs based on 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 Delavaud, Cotton, Akkar et al., 2012, Journal of Seismology Final logic tree

17 Greece - INSLAB earthquakes LLH versus frequency Atkinson&Boore 2003 McVerry et al Kanno et al Youngs et al Lin and Lee 2008 BCHydro 2010 Zhao et al Best fikng : Zhao et al [Japan] and Lin and Lee [Taiwan] 17

18 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

19 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) Expert elicitation Logic trees from 6 experts Testing using data Rankings of GMPEs based on 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

20 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

21 bold. Subduc0on Regions Category based on expert judgment Models Models supported by all the experts Atkinson and Boore (2003) Models supported by a majority of experts Youngs et al. (1997) Zhao et al. (2006) Category based on data-testing results Models Models supported by the testing Lin and Lee (2008) for long periods (T > 0.16s) Zhao et al. (2006) Models supported by the testing Atkinson and Boore (2003) for short periods (T apple 0.16s) Zhao et al. (2006) Category based on expert judgment Models and data-testing results Models supported by the data testing and the experts choices Models chosen by a majority Zhao et al. (2006) of experts and supported by the data-testing results Models chosen by a minority McVerry et al. (2006) of experts or with a low Atkinson and Macias (2009) data-testing result Garcia et al. (2005) Models not supported by the data-testing and not chosen by the experts Selected Models WS1 WS2 Zhao et al. (2006) Atkinson and Boore (2003) Youngs et al. (1997) Lin and Lee (2008) Delavaud, Cotton, Akkar et al., 2012, Journal of Seismology 21

22 Stable Con0nental Regions 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 Models supported by all the experts Campbell (2003) Toro et al. (1997) Models chosen by a majority of experts Atkinson and Boore (2006) Douglas et al. (2006) Atkinson (2008) Models chosen by a minority of experts Models not chosen by the experts Tavakoli and Pezeshk (2005) Selected models for shield WS Campbell (2003) 0.5 Toro (2002) 0.5 Selected models for continental crust WS Campbell (2003) adjusted to 800m/s 0.2 Toro (2002) adjusted to 800m/s 0.2 Akkar and Bommer (2010) 0.2 Cauzzi and Faccioli (2008) 0.2 Chiou and Youngs (2008) 0.2 Delavaud, Cotton, Akkar et al., 2012, Journal of Seismology 22

23 !"# "$%&'()) *+,,-. $/0123(/ 34 5,,% *+,,+9 1:&1');2<(/. $/0123(/ 34 5,,%62 =>>$8 $:/?4%%(8 "$1AA; $:/ B$CC;4); *+,,5. "<;41 $:/ D41:E2 *+,,5.,F+,F+,F+,F+,F+!G G<$4!" #$% *+,,H. =3>;:24: $:/?448( *+,,-. D41:E2!" #$% K;: $:/ K(( *+,,5.,FL,F+,F+,F+ =>>$8 $:/?4%%(8 =!"# 7-2 "$1AA; $:/ B$CC;4); *+,,5. G<$4!" #$% =!"# -2 < "$1AA; $:/ B$CC;4); *+,,5. "<;41 $:/ D41:E2 *+,,5.,FM,FM "<;41 $:/ D41:E2 *+,,5.,F+ Ocenanic crust: same logic tree as ASCR N4)C$:;C A4:(2 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

24 Key discussions Outline during the project Global or local models? What is a rock site? Beyond GMPE s? Removing the ergodic assump0on? Regionalisa0on of GMPE s Vs30/slope correla0ons

25 How to use local weak-motion data? Accelerometric data M w = Dist= km 25

26 What do regional data show us? Assumptions: Far-field approximation (Dist>15 km) Brune s type source (1970) Average radiation pattern v S constant along the path Geometrical decay constant between 15 and 200 km Q(f)=Q 0 f α Source Propagation Site Drouet et al., 2008, 2010

27 Stress- drop regional varia0ons in France Red: Alps Green: Rhine Grabben Blue: Pyrenees Drouet et al., 2008,

28 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 ( last accessed March 2012) are also plotted (black crosses). The solid lines represent the regional stress drops models used in the simulations. Right : comparison of teh stress drop model for the Alpes and the models for Switzerland (Edwards, 2012,

29 Unresolved question : reproducing large earthquake groundmotion decay given small magnitude earthquake decay Akkar and Bommer,

30 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 v S constant along the path Geometrical decay constant between 15 and 200 km Q(f)=Q 0 f α Source Propagation Site Drouet et al., 2008, 2010

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

32 Site part : kappa? Boore & Joyner (1997)

33 Kappa dependencies on Vs30 : «rock» site are heterogeneous! Van Houtte et al., BSSA, 2011

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

35 Van Houtte et al., 2011

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

37 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

38 Avoiding the specification of any a priori functional form, artificial neural networks (ANNs) provide fully data-driven predictive models 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-th neuron in the hidden layer, w o jk the synaptic weights between the j-th neuron of the hiddenb o k layer and the k-th neuron in the output layer, and the bias of the k-th neuron in the hidden layer. 38

39 The ANN approach was inspired by investigations into the structure of the human brain An ANN is made up of interconnecting artificial neurons, with the input and the hidden and output layers The synaptic weights and bias of an ANN are obtained by learning, through the training dataset, to minimize an error function equal to the sum of the squared residuals (MSE) in the output layer 39

40 KiK-net seismic database (3891 records from 398 sites and 335 earthquakes Derras et al. 2012, BSSA 40

41 Although not imposed a priori, ANN results have a number of physically sound features: clear magnitude and depth dependency of the decay of the ground motion with distance and near-fault saturation for large magnitudes, 41

42 Testing of the relative importance of the effects of independent variables on seismic ground motion sigma Figure 10. Bars represent the sensitivity of the input variables observed during the modeling. 42

43 Depth is shown to affect moderate magnitude events ground-motions in the epicentral area 43

44 The results show a clear increase in PGA with decreasing VS30 for given values of magnitude, distances, and f0. This corresponds to the classical observation that stiff-soil sites have lower acceleration than softer sites, at least at low acceleration levels 44

45 Comparison with three other recent attenuation relations derived for the Japanese region. 45

46 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 the prediction of the horizontal component of earthquake response spectra for 5% of critical damping, to other damping levels and vertical component 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. Publication : autumn

47 European strong-motion database Bulletin of Earthquake Engineering, special issue 47 Derras et al, 2013

48 Ground-motion predictions : Vs=760m/s, Rjb=10km Texte 48 Douglas et al., 2013

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

50 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-specific amplification and non-ergotic (single-station) aleatory variability source and path dependency of the aleatory variability

51 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...

52 Slight changes of the aleatory variability have a large impact on PSHA Increasing σ Bommer and Abrahamson (2006)

53 The observed variability has not decreased these last 30 years.. ~300 records ~3000 records Year σ[ln(y)] = 2.3σ[log 10 (y)] Strasser et al. (2009) 0.7 ln scale 0.3 log sc ale

54 Single station sigma : the ground-motion variability due to site-to-site variability is moved from the aleatory to the epistemic part SS Site to site variability variability within a site class (variability of site proper0es) need enough records to evaluate the site specific amplification factor! For site specific-studies only (and not PSHA maps)

55 Single-station within-event variability : 1000 stations (for which the site term has been evaluated from a minimum of 5 records) ss Single station PGA Ergodic Period (Sec) PGA Period (Sec) California Swiss Taiwan Turkey Japan All Data Rodriguez-Marek et al., 2013

56 Use of generic (non-region dependent) single-station sigma model within the Pegasos project Magic value of 0.45? ss Single station PGA Ergodic Period (Sec) PGA Period (Sec) California Swiss Taiwan Turkey Japan All Data Rodriguez-Marek et al., 2013

57 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-specific amplification and non-ergotic (single-station) aleatory variability source and path dependency of the aleatory variability

58 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: /SED SHARE,