Fault segmentation user perspective
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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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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

    • 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


Unknown Segmentation

  • Truncated exponential model for mag pdf is sometimes used as proxy for segmentation


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


Deterministic Approach(Median Sa)


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


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


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


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


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


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


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


Example from WG0394% Moment in Char Eqk


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


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


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


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


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


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.


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)


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


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


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


Probability of Surface Rupture (modified from IGNS, 2003)


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