Epistemic Uncertainty: Previous Approaches

Transcription

1 Epistemic Uncertainty: Previous Approaches N. Abrahamson NGA- East workshop Jul 14, 214 Epistemic Uncertainty: Discrete RepresentaFon of ConFnuous DistribuFon

2 Logic Trees for Ground MoFons Examples of SSHAC L3 and L4 Ground MoFon Studies TIP (199) Yucca Mtn (1998) EPRI (24) PEGASOS (24) BCHYDRO (2) SWUS (214) SSHAC Level 4 Type of QuesFon TIP (199) What is the median PSA at Hz for a magnitude 6 earthquake at a distance of 2 km for a rock site? Answer Scalar for each M,R,F pair DiscreFze a confnuous distribufon %, %, 9% values Mutually exclusive, collecfvely exhausfve (MECE)

3 TIP (199) Yucca Mtn SSHAC Level 4 Concept Experts would consider many discrete M,R cases and make sure that the epistemic distribufon was appropriate Type of QuesFon What is the median PSA at Hz for a magnitude 6 NML earthquake at a distance of km on the HW a rock site? About 6 (M,R) Pairs and 9 frequencies Answer Scalar for each case M,R,F Describe a confnuous distribufon (may be asymmetric) Mu, SigmaMu+, and SigmaMu- Mutually exclusive, collecfvely exhausfve

4 Yucca Mtn Used variable weights for GMPEs Parameterized as sigma Mu - Mag, dist, mech, period dependent Yucca Mtn DifficulFes Too many evaluafons needed (~4 cases H & V) Experts all used weights on alternafve models (empirical GMPEs, PS simulafons, FF simulafons) to develop an algorithm for determining the weights Algorithms based on relafve merits of models Answer intended to be a descripfon of stafsfcal distribufon Used symmetric distribufons Results treated as independent for each case No correlafon between cases (e.g. different mag or dist scaling)

5 EPRI (24) SSHAC level 3 Concept Define model clusters based on class of model with weights for each cluster Address stafsfcal uncertainty within each cluster QuesFon What is the weight for the cluster? What are the weights of a GMPEs within each cluster? Answer Vector PSA(M,R,F) Weights capturing relafve merits of clusters RepresentaFve set of GMPEs and weights that discrefze confnuous distribufon of GMPEs within each cluster DifficulFes Clusters evaluated for their merits, not for the range of ground mofon models sampled Not mutually exclusive, collecfvely exhausfve Creates new models (not published models) PEGASOS (24) SSHAC Level 4 Concept If experts using weights on GMPEs to make their evaluafon, then acknowledge this and directly ask about weights on GMPEs Type of QuesFon What at the weights on GMPEs for a given M,R,Freq range? M M7. Dist - km Freq.3 to Hz (plus ppga) Answer Vector PSA (M,R,F) M,R,Freq dependent weights for GMPEs and point source models DifficulFes No longer a simple discrefzafon of a confnuous distribufon Weights represent relafve merits of models Not mutually exclusive, collecfvely exhausfve

6 PEGASOS (24) Used mag- dist dependent Weights on GMPEs BCHYDRO (2) SSHAC Level 3 Concept Scaled backbone approach for subducfon earthquakes Weights on published GMPEs for crustal earthquakes QuesFon What are the weights on the GMPEs? Answer for subducfon zones Vector PSA for (M,R,F) Backbone GMPE Suite of scaled versions of the backbone GMPE Uncertainty scale factor increases for larger magnitudes StaFsFcal weights for the scaled GMPEs DiscreFze confnuous distribufon Mutually exclusive, collecfvely exhausfve Difficulty Does not address changes in the magnitude or distance scaling

7 REPORT No.: DSGN: PSHA7DS Fig CHKD: DATE: NAA NJG Nov 2 E68-3 Scaled Backbone Approach Advantages of scaled backbone Weights on logic trees Leads to models that are mutually exclusive Models are collecfvely exhausfve in terms of ground mofon levels Easier to treat GMPE weights as probabilifes Disadvantages of scaled backbone Does not capture uncertainfes in scaling (e.g. distance slope, magnitude slope) Leads to over- esfmafon of width of fracfles Creates new models (not published models)

8 SWUS (214) SSHAC Level 3 Concept QuesFon Answer Develop a representafve set of GMPEs that span the full range of technically defensible models Capturing changes in magnitude and distance scaling Mutually exclusive and collecfvely exhausfve in the Sammon s map space What are the GMPEs? What are the weights on the GMPEs? Vector PSA (M,R) for each F StaFsFcal weights for the GMPEs Difficulty Large number of GMPEs Independent at each frequency (does not capture correlafon across freq) SWUS Epistemic Uncertainty Discrete RepresentaFon of ConFnuous DistribuFon in 2 Dimensions 1 NGA T.3 1 NGA T.3 C C1 1 1 C1 1 SIM T.3 C2 C2 DiscreFze confnuous distribufon in Sammon s map space Mutually exclusive, collecfvely exhausfve

9 Summary Study Approach Resul2ng Models TIP Point esfmates (M,R,F) MECE for each point esfmate Yucca Mtn Point esfmates (M,R,F) MECE for each point esfmate EPRI PEGASOS GMPE clusters Suite of GMPEs within cluster GMPEs adjusted to site- specific condifons Merits weights for clusters MECE within cluster Merit weights for GMPEs BCHYDRO Scaled backbone MECE, but ignores correlafon of M, R scaling Hanford SWUS Scaled backbone with alternafve magnitude scaling Sampling GMPEs from Sammon s maps, independent for each freq MECE including correlafon of M scaling MECE including correlafon of M,R scaling