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Fault Segmentation: User Perspective

Fault Segmentation: User Perspective. Norm Abrahamson PG&E March 16, 2006. Segmentation Approaches in Seismic Hazard Practice. Pre ~1985 Used 1/3 to 1/2 fault length for the characteristic (max) earthquake Implies segmentation but unknown segmentation points 1985-1995

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Fault Segmentation: User Perspective

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  1. Fault Segmentation:User Perspective Norm Abrahamson PG&E March 16, 2006

  2. Segmentation Approaches in Seismic Hazard Practice • Pre ~1985 • Used 1/3 to 1/2 fault length for the characteristic (max) earthquake • Implies segmentation but unknown segmentation points • 1985-1995 • Geologist identified segmentation points • Used full length of segments for characteristic earthquake • Typically considered segmented or unsegmented cases through logic tree (e.g. epistemic uncertainty), but not aleatory variability in segmentation • 1995 - Current • Begin to include aleatory variability in segmentation (sometimes individual segment rupture, sometimes multi-segment ruptures

  3. Unknown Segmentation • Truncated exponential model for mag pdf is sometimes used as proxy for segmentation

  4. Example: Effects of SegmentationSlip-rate=10 mm/yr

  5. Deterministic Approach(Median Sa)

  6. Example: Effects of SegmentationPGA: Site 1 (at segmentation point)

  7. Example: Effects of SegmentationT=3: Site 1 (at segmentation point)

  8. Example: Effects of SegmentationPGA: Site 2 (middle of a segment)

  9. Example: Effects of SegmentationT=3: Site 2 (middle of a segment)

  10. User Needs for Segmentation • Fault Models • Need rates of ruptures on faults • Develop credible models of the fault behavior in terms of segmentation that are consistent with observations • Avoid Bias • Should not include intentional conservatism • Not clear what is conservative • Uncertainty • Uncertainty in segmentation is handled using alternative models of fault behavior • Should not just be single segments ruptures and all segment ruptures

  11. Truncated Exponential Modelfor faults • Hecker evaluated variability of surface slip at a point • Found that the truncated exponential model will produce much larger variability than observed • Concluded that truncated exponential model is not applicable to individual faults

  12. User Needs • Earthquake probabilities for ruptures are useful, but they need to be converted to PSHA inputs • More useable information is the equivalent slip-rate that for each rupture (single segment or multi-segment) • Allows direct input into standard PSHA codes

  13. Example from WG0394% Moment in Char Eqk

  14. Coefficient of Variation of Surface Slip at at Point (from Hecker)

  15. Testing of Magnitude Recurrence Models Using C.V. • Forward Modeling of expected observations of slip at a point • Prob (M) (from mag recurrence model) • Prob (rupture to surface given M) • Prob (rupture past site given Rup Length(M)) • Prob (amount of surface slip given M) • Prob (detection) including effect of adding slip from non-detected events to the detected events • Magnitude recurrence models • Truncated exponential • Youngs & Coppersmith Characteristic • Max Mag = 7.5, MinMag = 6.0

  16. Amount of Surface Slip • Average Displacement • Use Wells and Coppersmith for all fault types • log(AD) = -4.8 + 0.69M ± 0.36 (±0.82 ln units) • Variation in Displacement along Strike • Use results from (ref?) • Sigma along strike approx 0.7 natural log units • Total standard deviation of slip-at-a-point • Sqrt(0.822+0.702)=1.07

  17. C. V. from Modeling ResultsCase1:Using full Slip Variability for given M

  18. C. V. from Modeling ResultsCase1:Using reduced Variability for given M (reduced to 0.3 natural log units)

  19. Conclusions from Forward Modeling of Surface Slip • Variability of slip at a point must be much smaller than expected using global models • The Y&C mag recurrence model can give C.V. values similar to observed values if small variability in slip for given mag is used. • The truncated exponential mag recurrence model gives much larger C.V. values than observed even with reduced variability in slip given mag. • The truncated exponential model is not consistent with the observed C.V. values.

  20. Effect of Small Number of Events per Site • Use Monte Carlo • Population C.V.=0.4 • Same sampling as in data set • Result: • Average C.V.=0.41 (small bias toward larger CV)

  21. Event Position of Smallest Slip • Smallest slip is more often the most recent event • Accommodate effect by overprinting • Calibrate levels for overprinting (e.g. 40% of amplitude) using observed frequencies

  22. Effect of Probability of Detection and OverprintingExample: mean slip is close to detection thresholdresult: Increase in C.V.

  23. Effect of Probability of DetectionExample: mean slip is 2.5 times detection thresholdResult: Similar C.V.

  24. Probability of Surface Rupture (modified from IGNS, 2003)

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