Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
2008 Sichuan earthquake wikipedia , lookup
April 2015 Nepal earthquake wikipedia , lookup
2009–18 Oklahoma earthquake swarms wikipedia , lookup
2010 Pichilemu earthquake wikipedia , lookup
1880 Luzon earthquakes wikipedia , lookup
2009 L'Aquila earthquake wikipedia , lookup
1570 Ferrara earthquake wikipedia , lookup
1906 San Francisco earthquake wikipedia , lookup
San Andreas (film) wikipedia , lookup
Applications of Earthquake Simulators to Assessment of Earthquake Probabilities Jim Dieterich Some issues/limitations with current UCERF approach Sometimes I lie awake at night, and I ask, "Where have I gone wrong?” Then a voice says to me, "This is going to take more than one night." (Charles M. Schultz/Charlie Brown, in "Peanuts”) 1. Models have become exceedingly complex. 2. Probability density distributions for recurrence of slip in large earthquakes are not known. Statistics of large earthquakes very poorly defined. Poisson, quasi-periodic, clustered? Magnitude and position dependence of pdfs. 3. Interpretation of empirical model. 4. Strict use of characteristic earthquakes and segmentation is problematic 5. Point characterizations of segments. Properties governing recurrence and slip are not constant along segments. Stress interactions, clock reset. 6. Non-linear loading processes. viscoelasticity, fault creep, off-fault relaxation 7. Integration with spatiotemporal clustering 8. Fault to fault jumps and rupture branching. Contributing problem: Current approaches tend to treat these items independently, when in fact they are often coupled Ned’s Priorities: 1)Relax segmentation 2)Incorporate spatiotemporal clustering Inputs to simulators Simulators directly produce earthquake rate models for A- and B- type faults. Catalogs ~106 events. •Moment-balanced •Segmentation is not assumed or enforced •Multiple realizations effect of parameter uncertainties •Multiple models •Tuned to be consistent with paleoseismic recurrence •Rupture jumps and branching •Uncertainties in moment-area relations are largely avoided Direct prediction of conditional probabilities •Sufficient number of events in catalogs to generate empirical pdfs for all fault sub-sections •No apriori assumptions about clustering vs Poisson vs quasiperiodic. •Multiple realizations – evaluate effect of parameter uncertainties •Multiple models Subsection approach to determining conditional probabilities: Steps Probability density for recurrence of slip on fault sub-section k Pi CPi Ri CPk = conditional probability of event M≥6.5 on section k Sub-catalog of n events for a section that occur in the interval Dt for used to determine CPk Weighted participation rate of event i 1 Rki nk Ni j1 Sub-catalog of n events for a section that occur in the interval Dt for used to determine CPk 1/ N kj Weighted participation rate of event i Total probability of event i in the conditional interval Dt 1 Rki Ni Pi nk Ni 1/ N kj j1 Ni CPk CPk Rki k 1 n k k 1 Ni 1/ N j 1 k j Spatiotemporal clustering with RSQSim Stacked rate of seismicity relative to mainshocks 6>M<7 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 Inter-event Waiting Time Distributions Space – Time Distributions Earthquake cluster along San Andreas Fault M7.3 43 aftershocks in 18.2days All-Cal model – SCEC Simulator Comparison Project Earthquake cluster along San Andreas Fault M6.9 Followed by 6 aftershocks in 4.8 minutes All-Cal model – SCEC Simulator Comparison Project Earthquake cluster along San Andreas Fault M7.2 All-Cal model – SCEC Simulator Comparison Project Clusters of Large Earthquakes Robs is the total number of clusters M≥7, divided by the number of isolated M≥7 earthquakes Rates of M≥7 Earthquakes following M≥7 Earthquakes Cumulative probability of earthquake (on San Jacinto fault segment of the San Jacinto fault) From random time From time of M≥6.5 on adjacent Anza segment M≥6.5 on San Jacinto Segment From random time From time of M≥6.5 on distant Calaveras segment M≥6.5 on San Jacinto Segment Cumulative probability of earthquakes (on San Jacinto fault segment of the San Jacinto fault) From random time From time of M≥6.5 on adjacent Anza segment M≥6.5 on San Jacinto Segment From random time From time of M≥6.5 on adjacent Anza segment M≥5.5 on San Jacinto Segment Summary of some advantages of simulators relative to current UCERF methods • Integrated self-consistent framework for generating an earthquake rate model • Properly captures intrinsic relations between stress and fault slip in 3D systems and avoids the dubious use of point characterizations of spatially varying properties (stress, slip, time since last slip, clock reset) • Clustering is modeled deterministically and tied to constitutive parameters and evolving stress conditions • Framework for characterizing regional fluctuations of seismicity rates – interpretation of empirical model • Non-linear stressing from interactions with deep creeping zone and viscoelasticity in some models • Moment balancing issues are eliminated • No assumptions are made regarding characteristic earthquakes (pro or con) • Rupture jumps and branching occur spontaneously Summary of Possible Near-Term Applications 1) Earthquake rate models for A and B faults (no a priori segmentation) 2) Conditional probabilities on A and B faults (Poisson, clustering and quasi-periodic are seen) 3) Clustering probabilities for moderate and large earthquakes (Pre-calculate look-up table for near real-time response) Some other applications • Develop and/or test algorithms and models used with current methods • Interpretation of empirical model and regional rate fluctuations (Tullis talk) • Relative weighting appropriate for quasi-periodic, clustering, and Poisson probability models • Evolution of b-values • Evaluate fault rupture scenarios (“Stringing Pearls” of Biasi and Weldon – Goal: reduce number of possible models)