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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

Fault Segmentation:User Perspective

Norm Abrahamson


March 16, 2006

segmentation approaches in seismic hazard practice
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
Unknown Segmentation
  • Truncated exponential model for mag pdf is sometimes used as proxy for segmentation
user needs for segmentation
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 model for faults
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
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
testing of magnitude recurrence models using c v
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
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 reduced Variability for given M (reduced to 0.3 natural log units)
conclusions from forward modeling of surface slip
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
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
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.