some general implications of results

15
ome General Implications of Resul Because hazard estimates at a point are often dominated by one or a few faults, an important metric is the participation MFD for each fault (we aggregate results from our ~2,000 subsections back onto the ~315 parent sections to keep things manageable). Fault-section MFD plots over all Char logic-tree branches are available here: http://opensha.usc.edu/ftp/kmilner/ ucerf3/2012_10_14-fm3-logic-tree-sample-x5_run0/ parent_sect_mfds/ http://opensha.usc.edu/ftp/kmilner/ ucerf3/2012_10_14-fm3-logic-tree-sample-x5_run0/ parent_sect_mfds/ cumulative_nucleation_mfd_comparisons.csv Some examples…

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Some General Implications of Results. Because hazard estimates at a point are often dominated by one or a few faults, an important metric is the participation MFD for each fault (we aggregate results from our ~2,000 subsections back onto the ~315 p arent sections to keep things manageable). - PowerPoint PPT Presentation

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Page 1: Some General  Implications of Results

Some General Implications of Results

Because hazard estimates at a point are often dominated by one or a few faults, an important metric is the participation MFD for each fault (we aggregate results from our ~2,000 subsections back onto the ~315 parent sections to keep things manageable).

Fault-section MFD plots over all Char logic-tree branches are available here:

http://opensha.usc.edu/ftp/kmilner/ucerf3/2012_10_14-fm3-logic-tree-sample-x5_run0/parent_sect_mfds/

http://opensha.usc.edu/ftp/kmilner/ucerf3/2012_10_14-fm3-logic-tree-sample-x5_run0/parent_sect_mfds/cumulative_nucleation_mfd_comparisons.csv

Some examples…

Page 2: Some General  Implications of Results

Mean & +/- StdevOfMean

+/- Stdev

Min & Max

Cumulative Mean

Participation MFD for 100 simulated annealing runs for the same branch and same equation set weights

Page 3: Some General  Implications of Results

Participation MFD for 100 simulated annealing runs for the same branch and same equation set weights

Mean & +/- StdevOfMean

+/- Stdev

Min & Max

Cumulative Mean

Page 4: Some General  Implications of Results

Mean, Min, and Max from all logic-tree branches

UCERF3 Mean

UCERF3 Mean Cumulative

UCERF2

Page 5: Some General  Implications of Results

Mean from all logic-tree branches

Participationvs

Nucleation

Page 6: Some General  Implications of Results

Mean, Min, and Max from all logic-tree branches

UCERF3 Mean

UCERF3 Mean Cumulative

UCERF2

Page 7: Some General  Implications of Results

Mean, Min, and Max from all logic-tree branches

UCERF3 Mean

UCERF3 Mean Cumulative

UCERF2

Page 8: Some General  Implications of Results

Other Aggregate Metrics (ERF based)

Not Yet Computed (1,440 takes time)

Page 9: Some General  Implications of Results

Hazard Map Comparisons

NSHMP Fortran code OpenSHA code

NSHMP 2008 (UCERF2)

Page 10: Some General  Implications of Results

Building-Code Implications via Risk Targeted Ground Motions (RTGM)

Figure 30. Comparison of probabilistic risk-targeted ground motions for 0.2-sec spectral acceleration at three California cities. In each plot the dark blue bins represent the summed weights of different RTGM values across the 480 UCERF2 time-dependent, logic-tree branches. The green Line represents the average UCERF2 value, the orange line represents the official values from the US Seismic Design Maps, (http://geohazards.usgs.gov/designmaps/us) and the four lines labeled “U3 …” represent UCERF3 Characteristic reference branches for all four deformation models. The RTGM values were computed using the weighted combination of the three Next Generation Attenuation relationships (NGAs) that were used in the 2008 NSHMP. Mean UCERF2 values are generally lower than the US Design Map RTGM because the former does not consider additional epistemic uncertainty on ground motion that was included in the 2008 NSHMP.

Page 11: Some General  Implications of Results

Statewide Portfolio Loss Analyses (Porter et al., in press in SRL)

OpenSHA

Page 12: Some General  Implications of Results

UCERF2 EAL tornado diagram

0.5 1.0 1.5 2.0

B-Faults b-value

Fault-slip rates

Deformation model

Connect more B faults?

A-fault solution type

Magnitude-area relationship

GMPE

Probability model

Expected annualized loss, $ billion

Empirical BPT, 0.3

CB2008 AS2008

A priori

Hanks & Bakun

False

D2.2

Unsegmented

0.8

Mo-rate bal

Ellsworth

True

D2.6

Segmented

0.0Median and weighted avg

Page 13: Some General  Implications of Results

0.5 1.0 1.5 2.0

B-Faults b-value

Fault-slip rates

Deformation model

Connect more B faults?

A-fault solution type

Magnitude-area relationship

GMPE

Probability model

Expected annualized loss, $ billion

Empirical BPT, 0.3

CB2008 AS2008

A priori

Hanks & Bakun

False

D2.2

Unsegmented

0.8

Mo-rate bal

Ellsworth

True

D2.6

Segmented

0.0Median and weighted avg

UCERF3 Char Branch

UCERF2 EAL tornado diagram

This capability will allow us to potentially trim non-important branches, plus accommodate those that remain.

Page 14: Some General  Implications of Results

What about aftershocks?NSHMP has removed aftershocks using the Gardner-Knopoff declustering algorithm (Gardner and Knopoff, 1974).

For our RELM region, 56% of M>5 events are main shocks and 44% are aftershocks according to this definition (Felzer, Appendix I)

GR b-value is 0.8 for declustered catalog (compared to 1.0).

We can apply this “filter” to UCERF3 ERFs

Page 15: Some General  Implications of Results

Now

Some Hazard Implications – Peter Powers