112/16/2010AGU Annual Fall Meeting - NG44a-08 Terry Tullis Michael Barall Steve Ward John Rundle Don Turcotte Louise Kellogg Burak Yikilmaz Eric Heien Jim Dieterich Keith Richards-Dinger Fred Pollitz Ned Field Olaf Zielke Ramon Arrowsmith Preliminary Results From SCEC Earthquake Simulator Comparison Project

212/16/2010AGU Annual Fall Meeting - NG44a-08 Purpose and Nature of Project To understand hazard better, we desire a statistical description of earthquakes for thousands of years –Instrumental, historic, and paleoseismic records are too short and/or too incomplete Earthquake simulators can generate long histories However, it is still unclear how realistic these are We are comparing the results for 5 earthquake simulators that can incorporate many faults We also compare with available observational data This present talk is our first preliminary report and only covers Northern California strike-slip faults

312/16/2010AGU Annual Fall Meeting - NG44a-08 Common Inputs / Different Approaches We have developed a common set of input and output formats –Thus all the simulators read the same input data –The output can be processed identically and the results compared directly –Have some tools for processing output; are developing more Relatively well-known input is fault geometry and slip rates We assume variations in fault stress drops (“strength”), but constraints on this are not good Simulators differ in many ways –Representation of fault friction is one big difference –Another is whether/how dynamic triggering is approximated

412/16/2010AGU Annual Fall Meeting - NG44a-08 Northern CA paleoseismic sites UCERF 2 Preferred recurrence intervals in yrs Calaveras fault, North 484 Hayward fault, North 401 Hayward fault, South 150 N. San Andreas, Vendanta 248 SAF, Arano Flat 106 N. San Andreas, Fort Ross 288 San Gregorio, North 0 (San Francisco 0) (Berkeley Campus 0) UCERF 2 Preferred recurrence intervals in yrs: norcal1 fault model

512/16/2010AGU Annual Fall Meeting - NG44a-08 Space – Time Plot ALLCAL norcal1 There is a wide range of event sizes, even on one fault section

612/16/2010AGU Annual Fall Meeting - NG44a-08 VIRTCAL Space – Time Plot norcal1 VIRTCAL has fewer, larger, more regular events. This is due to assuming significant dynamic stress triggering.

712/16/2010AGU Annual Fall Meeting - NG44a-08 Frequency Magnitude, Cumulative RSQSim ALLCAL VIRTCAL b = 1 Zielke Pollitz Our Entire N CA Fault System norcal1 All California observed seismicity, excluding Cascadia (UCERF2)

812/16/2010AGU Annual Fall Meeting - NG44a-08 Blue line is Wells and Coppersmith relationship RSQSim VIRTCALALLCAL Length - Slip Scaling norcal1

912/16/2010AGU Annual Fall Meeting - NG44a-08 RSQSim VIRTCALALLCAL Moment - Area Scaling Blue line is Wells and Coppersmith relationship norcal1

1012/16/2010AGU Annual Fall Meeting - NG44a-08 RSQSim VIRTCALALLCAL Moment - Length Scaling norcal1

1112/16/2010AGU Annual Fall Meeting - NG44a-08 Moment and Event Rates RSQSim Pollitz VIRTCALALLCAL M5 M6 M7 M5 M6 M7 M5 M6 M7 M5M6 M7 Note that 500 yr intervals can be found that differ significantly from other 500 yr intervals 500 yrs norcal1

1212/16/2010AGU Annual Fall Meeting - NG44a-08 Entire System ALLCAL PDFs of Inter-event Times Note that M5-6.5 events may not show up in the paleoseismic record – thus next slides will only show >= M7 norcal1

1312/16/2010AGU Annual Fall Meeting - NG44a-08 Probability Distribution Functions of Inter-event Times, M >= 7.0 Red lines show paleoseismic preferred interevent times from UCERF2 RSQSim VIRTCAL Zielke ALLCAL North Hayward FaultSouth Hayward Fault Note that the simulations show similar PDFs N & S, but the actual repeat times differ on N & S parts of fault norcal1

1412/16/2010AGU Annual Fall Meeting - NG44a-08 North Hayward FaultSouth Hayward Fault Probability Distribution Functions of Inter-event Times, M >= 6.5 (black), 7.0 (red) for ALLCAL, “tuned” by Steve Ward Can Adjust Inter-Event Times by Changing Fault “Strengths” Using Initial values of fault strengths: Using Values of fault strengths “tuned” to match inter- event times: Vertical red lines show paleoseismic preferred interevent times from UCERF2 norcal4 has more faults, including thrust faults Shifted N to longer and S to shorter times Initial N and S times about the same Fault “strength” increasedFault “strength” decreased

1512/16/2010AGU Annual Fall Meeting - NG44a-08 Probability Distribution Functions of Inter-event Times, M >= 7.0 Red lines show paleoseismic preferred interevent times from UCERF2 Note that the PDFs differ for different simulators for the same set of fault “strengths.” So each simulator needs individual tuning. norcal1 RSQSim VIRTCAL Zielke ALLCAL At Fort RossAt San Arano San Andreas Fault:

1612/16/2010AGU Annual Fall Meeting - NG44a-08 Entire System PDFs of Inter-event Times, Log-time scale Blue curves are Poisson process with same average rate as data norcal1 Plots all for 5 <= M <= Inf Zielke ALLCAL Pollitz VIRTCAL RSQSim Note that different simulators allow different inter-event times. Dealing with aftershocks needs short times. Omori’s Law gives a straight line

1712/16/2010AGU Annual Fall Meeting - NG44a-08 Time vs. Slip Predictability Time Slip Time Predictable Slip in Previous Event is linear with Interevent time Current event Time Slip Slip Predictable Slip in Current Event is linear with Interevent time Current event Both are true for a perfectly characteristic earthquake

1812/16/2010AGU Annual Fall Meeting - NG44a-08 ALLCAL RSQSim VIRTCAL Time and Slip Predictability – N Hayward fault norcal1 Time Predictable: Slip Predictable: Behavior differs for different simulators. For ALLCAL time predictable fits slightly better. For others at this location, slip is nearly independent of inter-event time.

1912/16/2010AGU Annual Fall Meeting - NG44a-08 ALLCAL RSQSim VIRTCAL Time and Slip Predictability – SAF, Arano Flat norcal1 Time Predictable: Slip Predictable: Behavior differs for different simulators. For ALLCAL time predictable fits slightly better. Again, for VIRTCAL, slip is nearly independent of inter- event time. For RSQSim there is some dependence at this location.

2012/16/2010AGU Annual Fall Meeting - NG44a-08 Conclusions The simulators show general agreement on many statistical measures However, significant differences exist between them At this preliminary stage of the project most simulators have not been “tuned” to match observations of inter- event times, so they can do better for that once input fault “strengths” are varied Many other statistical measures will be developed, for example to examine how often ruptures jump from one fault to another. We also study this for only two faults. We are making good progress on an All California fault model, following this N California smaller example