PEER Directivity End-Users Panel

Transcription

1 2016 COSMOS Annual Meeting and Technical Session Program 18 November 2016 PEER Directivity End-Users Panel Dr. Yousef Bozorgnia, P.E., F. ASCE, U.C. Berkeley Dr. Jennifer Donahue, P.E., Geosyntec Consultants

2 Background and motivation Directivity effects for near-fault area can be strong Example: 1992 Landers earthquake Latitude Landers, CA, EQ Lucerne Valley 136 cm/sec Rupture propagation Forward directivity region Epicenter 34 Backward directivity region Joshua Tree 43 cm/sec Longitude Bozorgnia-Campbell, 2004 Adopted from Somerville et al, 1997

3 Background: NGA-West2 Five ground motion prediction equations (GMPEs, attenuation models ), plus five directivity models were developed NGA-West2 directivity models included broadband and narrow-band models Directivity models were generally consistent for the case of strike-slip faulting Directivity models, however, were inconsistent for reverse slip Thus, leading to lack of implementation and general confusion about utilization in hazard analysis

4 Background: NGA-West2 NGA-West2 GMPEs, except one, did not explicitly include directivity effects Considering that some ground motion records, used in the development of GMPEs, included directivity effects, it was not clear: If the NGA-West2 GMPEs implicitly include some directivity effects, and to what degree i.e., were GMPEs biased with respect to directivity effects? Also it was not clear how to merge directivity models and GMPE models to be used in hazard analysis

5 In current practice Sometimes a directivity model is applied deterministically to include the effects on the response spectra Sometimes (not always) the location of hypocenter is randomized for one directivity model, to capture a more realistic effects of directivity Repeatedly we have received comments from end-users to provide better guidance on inclusion of directivity effects

6 PEER Directivity Panel Considering all technical issues for including directivity effects, PEER assembled a panel of end-users to review the current status and implementation of the directivity effects, and make recommendations for short-term and longterm Directivity modelers were not part of the panel To avoid any appearance of conflict But were continuously contacted to make sure the panel understands their models

7 PEER Directivity Panel Jonathan Stewart (Chair), UCLA Yousef Bozorgnia, UC Berkeley Jonathan Bray, UC Berkeley Jennifer Donahue, Geosyntec Consultants Robert Graves, USGS I. M. Idriss, UC Davis Nico Luco, USGS Stephen Mahin, UC Berkeley Tom Shantz, CALTRANS

8 Charge of the PEER Directivity Panel To clarify the technical issues To develop guidelines on the application of NGA- West2 directivity models for hazard calculations To provide recommendations for targeted research to improve capabilities for directivity modeling in future GMPE development

9 Status of Directivity Panel Various comparisons and test runs have been made In collaboration with directivity modelers, examples of implementing different methodologies for hazard calculations have been carried out The final report is being drafted The report will be published by February 2017

10 PEER NGA-WEST2 DIRECTIVITY MODELS

11 NGA-West2 Directivity Models Team led by Paul Spudich. Five models: 1. bay13: Bayless and Somerville cscy: Chiou and Spudich/Chiou and Youngs row13: Rowshandel sb13: Shahi and Baker sc13: Spudich and Chiou 2013

12 NGA-West2 Directivity Models Set out to solve aforementioned problems: Centering: Directivity parameters regressed by including term in GMPE development (cs13, sb13) among NGA-W2 GMPEs, only applies to CY14 SB14 used functional form from NGA-W1 (CB08)

13 NGA-West2 Directivity Models Set out to solve aforementioned problems: Centering: Directivity parameters regressed by including term in GMPE development (CS13, SB13) among NGA-W2 GMPEs, only applies to CY14 SB14 used functional form from NGA-W1 (CB08) Models developed for a posteriori modifications of RotD50 in such a way that predictions are centered with regard to hypocenters and racetracks (row13, sc13). If present, is near-fault GMPE bias accounted for?

14 NGA-West2 Directivity Models Set out to solve aforementioned problems: Centering Fault length given in non-normalized dimensions

15 NGA-W2 Directivity Models bay13 Bayless & Somerville The bay13 model is parameterized similarly to the Somerville et al. (1997) model, except that the dimension of the fault rupturing towards the site is used without normalization. Theta Site L s Epicenter

16 NGA-W2 Directivity Models cscy Chiou & Spudich/Chiou & Youngs The cscy model is rooted in isochrone theory (Bernard and Madariaga 1984; Spudich and Frazer 1984, 1987), with the fault represented as a line source between hypocenter and the direct point (P D ) Fault slip along the line source occurs at the rake angle. The effects of slip direction are accounted for by a radiation pattern term computed as the average over the source length. Directivity is associated with the component of slip that aligns with the line source.

17 NGA-W2 Directivity Models row13 - Rowshandel The row13 model uses a geometric parameter computed as the weighted sum of two dot products for a given section of the fault: (1) fault-to-site vector and vector representing direction of rupture; and (2) fault-to-site vector and slip vector. When these weighted sums for a given section of the fault are summed over the fault surface (for a given site), directivity parameter ξ is obtained.

18 NGA-W2 Directivity Models sb13 Shahi & Baker The sb13 model describes the effects of pulses on PSA; for forward application, the effect is scaled by a model for pulse probability. Pulse probability, in turn, is related to geometric parameters, although like bay13, the fault dimension rupturing towards the site is not normalized.

19 NGA-W2 Directivity Models sc13 Spudich and Chiou The sc13 model is similar to cscy in its basis in isochrone theory. While the E-path in below is not used, the directivity parameter is computed from the along-fault distance D between points PH (hypocenter) and the closest point (PC). Radiation pattern is taken from point PH and directivity results from the component of slip that aligns with the PH-to-PC path.

20 Centering Directivity model centering refers to whether they produce on average, a null change in ground motion. Centering is accomplished in directivity model development by ensuring that if the directivity term calculated for a rupture were averaged over potential hypocenters and racetracks having constant rupture distance, the resulting average should be zero. If the GMPE is itself centered, then we want the directivity term to also be centered. If GMPE is biased towards forward directivity, then if the directivity term is centered we retain the bias.

21 DPP = DPP DPP

22 DPP DPP DPP = 2.36

23 DPP DPP DPP =

24 DPP = DPP DPP ΔDPP

25 DPP = DPP DPP f(δdpp) *after Eq 7, C&Y2014

26 Centering 2 Approaches 1. Compare GMM predictions for all GMPE models to CY (centered by inclusion of a Directivity Parameter (DP))

27 Records used by the 5 NGA-W2 GMPE Modelers Centering 2 Approaches 1. Compared GMM predictions for all models to CY (centered by inclusion of a Directivity Parameter (DP))

28 Centering 2 Approaches 1. Compare GMM predictions for all models other than to CY (centered by inclusion of a Directivity Parameter (DP))

29 Centering 2 Approaches 1. Compare GMM predictions for all GMPE models to CY (centered by inclusion of a Directivity Parameter (DP)) 2. Statistical review of ΔDPP. As an independent check of centering, we examined the distribution of directivity parameter ΔDPP for events contributing data. We computed three mean ΔDPP values : Mean of all ΔDPP values in flatfile: (75 events) Mean of ΔDPP values for events with 10 recordings: (40 events) Mean of ΔDPP values for events with 10 recordings and Rrup < 40 km: (30 events)

30 ΔDPP

31 Centering 2 Approaches 1. Compare GMM predictions for all GMPE models to CY (centered by inclusion of a Directivity Parameter (DP)) 2. Statistical review of ΔDPP. As an independent check of centering, we examined the distribution of directivity parameter ΔDPP for events contributing data. We computed three mean ΔDPP values as follows: Mean of all ΔDPP values in flatfile: (75 events) Mean of ΔDPP values for events with 10 recordings: (40 events) Mean of ΔDPP values for events with 10 recordings and Rrup < 40 km: (30 events) Our conclusion, based on the model comparisons and mean ΔDPP checks, is that non-directive NGA-West2 models ASK, BSSA, and CB can be considered to reflect a directivity-neutral condition

32 MODEL COMPARISONS

33 Strike-Slip Comparisons M7.8, T = 1 sec bay13 cscy row13 sb13 sc13

34 Strike-Slip Comparisons M7.8, T = 3 sec bay13 cscy row13 sb13 sc13

35 Strike-Slip Comparisons M7.8, T = 5 sec bay13 cscy row13 sb13 sc13

36 Strike-Slip Comparisons M7.8, T = 10 sec bay13 cscy row13 sb13 sc13

37 Reverse Comparisons M7, Dip 45, T = 1 sec bay13 cscy row13 sb13 sc13

38 Reverse Comparisons M7, Dip 45, T = 3 sec bay13 cscy row13 sb13 sc13

39 Reverse Comparisons M7, Dip 45, T = 5 sec bay13 cscy row13 sb13 sc13

40 Reverse Comparisons M7, Dip 45, T = 10 sec bay13 cscy row13 sb13 sc13

41 Comments on Models Strike-Slip Models are consistent Significant model-to-model variability for Reverse models cscy, row13, and sc13 consider the component of slip in the direction of rupture bay13, sb13 only consider fault dimension in the direction of slip for reverse events. For oblique-slip scenarios, the directivity pattern is dominated by the strike-slip component.

42 Initial Recommendations for Known Hypocenter Models Currently the panel is leaning towards advocating: cscy, row13, and bay13 Considerations: Does the physical parameterization make sense? How well documented is the model? Has the model been applied previously? Does the collection of recommended models reasonably capture epistemic uncertainty?

43 Initial Recommendations for Known Hypocenter Models The panel recommends discontinuing Somerville et al. (1997) with the Abrahamson (2000) modifications The models are developed from residuals of late-1990s era GMMs, which are not properly centered for near-fault conditions. As with all other ground motion models from that era, that vast increase in the size and quality of data developed that time make the current models obsolete. The parameterization using dimensionless (normalized) fault lengths is now recognized as non-physical, which has been corrected in the NGA-West2 directivity models. sb13: This model links directivity to pulse probability. These two issues are not necessarily fully correlated. sc13: effectively it is replaced by cscy.

44 MODEL IMPLEMENTATION WITH RANDOMIZED HYPOCENTER

45 Four Options Option 0: Generic PSHA with GMPE that does not consider directivity or has DPP =0 and similar for other directivity parameters Option 1: Composite distribution. Modifies GMM mean and standard deviation using location-specific look up tables. Option 2: Hazard computation with GMM that includes directivity parameter. Integration over distribution of directivity parameter within hazard integral. No integration over hypocenters. Option 3: Hazard computation with GMM that includes directivity parameter. Integration over hypocenters.

46 Four Options: Application Option 1: Can be used with any NGA-West2 GMM, but only two options for the directivity adjustment at this time (from cscy and row13) Options 2-3: Most self-consistent is cscy. Can also be used with other NGA-West2 GMMs combined with directivity models (through addition in ln units).

47 Approach 1: Composite Source This method results in a change in the median spectral acceleration ( µ) and σ dir Currently Two Models JWL16 Row15

48 Approach 1: JWL16 JWL16 Develop a simple model of additional mean and standard deviation to add to existing published ground motion prediction equations to account for this. Uses the directivity parameter DPP (Chiou and Youngs, 2014) Uses three hypocenter distribution models SS Along Strike Reverse Along Strike Both Down Dip

49 Approach 1: Row15 Row15 Similar to Row13 model but loops over random hypocenters. Uses uniform hypocenter distribution, along strike and down dip Period dependent

50 Strike-Slip Comparison JWL16 Row15

51 Reverse Comparison JWL16 Row15

52 Histograms

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61 Approach 1: Implementation A posteriori approach (outside hazard integral): 1. Perform ordinary hazard analysis; 2. Disaggregate the hazard at the point of interest, find the relative contribution to the hazard from the proximate fault denoted RC ; 3. From disaggregation, find the epsilon (ε) for the fault of interest from Step 2; 4. New IM level computed as (assumes μ in natural log units): σσ2 ( μμ+ εε ln nnnnnn GGGGGG = ln oooooo GGGGGG + RRRR ee µ 2 + σσ dddddd σσ 2 ) σ dir SA

62 Approach 2: Integration over DP The randomization occurs over a loop in the directivity parameter within the hazard integral. This approach considers the directivity parameter as distribution of randomized hypocenter locations. with no consideration of alternate hypocenter locations. Example: First calculate DDDDDD outside of the hazard integral. Create a PDF of the DPP as a function of M, R, Rx, Ry, etc for use inside the hazard integral. µ DPP Example: dd DDDDDD dddddddd MM RR DDDDDD σ DPP DPP

63 Approach 3: Integration over hypocenters The directivity is computed by randomizing the hypocenter within the hazard integral. More computationally demanding than Approach 2. dddddddddddd dddddddddddd dddddddd MM RR HHHHHHHHHH HHHHHHHHHH

64 Initial Recommendations for Randomized Hypocenter Models 1. All three approaches recommended. 2. Parametric studies comparing results between methods ongoing 3. Approach 1 has advantage of not requiring implementation in hazard codes. 4. Implementation of Approaches 2 and 3 in hazard codes is pending (other than Norm s)

65 Thank you