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RSQSim. Jim Dieterich Keith Richards-Dinger. UC Riverside. Funding: USGS NEHRP SCEC. Representation of Fault Friction. Constitutive relation: State evolution: Stress evolution: Terms in red are additional ones due to normal stress variations (Linker and Dieterich, 1992)

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rsqsim

RSQSim

Jim Dieterich

Keith Richards-Dinger

UC Riverside

Funding:

USGS NEHRP

SCEC

representation of fault friction
Representation of Fault Friction
  • Constitutive relation:
  • State evolution:
  • Stress evolution:
  • Terms in red are additional ones due to normal stress variations (Linker and Dieterich, 1992)
  • Interaction coefficients, K, calculated from the dislocation solutions of Okada, 1992
  • Tectonic stressing rates derived from backslipping the model
  • Numerical integration too slow for the scale of problems we would like to address
representation of fault friction1

State 0: locked fault

State 2: seismic slip

Representation of Fault Friction
  • Constitutive relation:
  • State evolution:
  • Stress evolution:

State 1: nucleation

representation of fault friction2
Representation of Fault Friction
  • No predetermined failure stress or stress drop
  • Stress drop scales roughly as
representation of fault friction3
Representation of Fault Friction
  • No predetermined failure stress or stress drop
  • Stress drop scales roughly as
approximations to elastodynamics
Approximations to Elastodynamics

Parameters that influence the rupture process:

  • Slip speed during coseismic slip determined from shear impedance considerations
  • Reduction of a on patches nearby to seismically slipping patches
  • Stress overshoot during ruptures
slide7

Effect of Overshoot on Rupture Characteristics

Large overshoot (13%)

Small overshoot (1%)

approximations to elastodynamics1
Approximations to Elastodynamics

Values for rupture parameters determined by comparison with fully dynamic rupture models

DYNA3D – Fully dynamic finite element simulation

Propagation time 14.0 s

RSQsim – Fast simulation

Propagation time 14.3 s

representation of viscoelasticity afterslip
Representation of Viscoelasticityafterslip
  • Rate-strengthening (a > b) patches
    • Approximated as always sliding at steady-state
    • Distributed as
      • Deep creeping extensions to major faults
      • Shallow creep on major faults
      • Entire creeping sections (e.g. SAF north of Parkfield)
        • Possibly with small imbedded stick-slip patches
      • More complicated mixed stick-slip and creeping areas (e.g. Hayward Fault)
representation of viscoelasticity afterslip1
Representation of Viscoelasticityafterslip

Penetration of slip of large events into creeping zone

representation of viscoelasticity afterslip2
Representation of Viscoelasticityafterslip

Fraction of moment release in creeping section

Aftershocks

representation of viscoelasticity afterslip3
Representation of Viscoelasticityafterslip

Small repeating earthquakes

Simulation

1989 Loma Prieta Earthquake

power law temporal clustering
Power-law temporal clustering

Stacked rate of seismicity relative to mainshock origin time

Decay of aftershocks follows Omori power law t -p with p = 0.77

Foreshocks (not shown) follow an inverse Omori decay with p = 0.92

Dieterich and Richards-Dinger, PAGEOPH, 2010

power law temporal clustering1
Power-law temporal clustering

Interevent Waiting Time Distributions

California Catalog 1911 – 2010.5

power law temporal clustering2
Power-law temporal clustering

Space – Time Distributions

slide17

Earthquake cluster along San Andreas Fault

M7.3

43 aftershocks in 18.2days

All-Cal model – SCEC Simulator Comparison Project

slide18

Earthquake cluster along San Andreas Fault

M6.9

Followed by 6 aftershocks in 4.8 minutes

All-Cal model – SCEC Simulator Comparison Project

slide19

Earthquake cluster along San Andreas Fault

M7.2

All-Cal model – SCEC Simulator Comparison Project

slide20

Colella et al.,

submitted

Slow-slip events

  • slip ~2.3 - 4.0 cm
  • duration ~10-40 days
  • inter-event time - ~10-19 months
  • simultaneous slip in different areas
  • no Omori clustering
  • spontaneous segmentation
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