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R. Console, M. Murru, F. Catalli

R. Console, M. Murru, F. Catalli Applying the Rate-and-State friction law to an epidemic model of earthquake clustering. 4th International Workshop on Statistical Seismology (Statsei4) in memory of Tokuji Utsu

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R. Console, M. Murru, F. Catalli

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  1. R. Console, M. Murru, F. Catalli Applying the Rate-and-State friction law to an epidemic model of earthquake clustering 4th International Workshop on Statistical Seismology (Statsei4) in memory of Tokuji Utsu Graduate University for Advanced Studies,Shonan Village Campus, Kanagawa Prefecture, Japan 9 - 13 January, 2006

  2. Outline of the talk An example of stochastic model: the classic epidemic model for earthquake clustering A modified stochastic model incorporating the rate-and-state constitutive law Towards the full application of the rate-and-state constitutive law to the epidemic model Problems still open

  3. Definition of the occurrence density and of the likelihood in the case of a continuous distribution

  4. Time dependent model (epidemic model) - I • The magnitudedistribution is the same for all earthquakes (Gutenberg-Richter law) The occurrence rate density is the superposition of a time independent (poissonian) component and the activity triggered by previous earthquakes • The occurrence rate of triggered events depends exponentially on the magnitude of every preceeding event • The spatialdistribution of triggered events is described by an isotropic function around the epicenter of every previous event • The temporal behaviour of triggered events is described by the Omori law starting from the occurrence time of every previous event

  5. Time dependent model (epidemic model) - II • Every event is potentially triggered by all the previous events and every event can trigger subsequent events according to their relative time-space distance • A definition of the words foreshock, mainshock and aftershock is not necessary

  6. Time dependent model (epidemic model) – III (Ogata, 1998) Occurrence rate density

  7. Time dependent model (epidemic model) - IV l(x,y,t,m) = fr ·l0(x,y,m) + li(x,y,t,m) ti<t Time independent distribution of the epicenters and magnitude for the spontaneous seismicity l0( x, y, m ) = m0( x, y )10 - b(m-m0) where m0is the completeness magnitude of the catalog

  8. Observed seismicity and its continuous representation (function m0(x, y), events per year in 1000 km2) (Central Italy, January 1981 – December 1996, M  2.0)

  9. Time dependent model (epidemic model) - V l(x,y,t,m) = fr · l0(x,y,m) + li (x,y,t,m) ti<t Time dependent distribution of the epicenters and magnitude for triggered seismicity where and

  10. Seismic sequence of Umbria-Marche (1997) Comparison between the number of observed events (a) and the number of expected events (b) for time windows of 12 hours

  11. Epidemic model Umbria-Marche, 1 September 1997, 00:00 1 6 0 1 4 0 10 1 1 2 0 0.1 ) m k 0.01 ( Y 0.001 1 0 0 0.0001 1E-005 8 0 1E-006 6 0 -60 -40 -20 0 20 40 X (km) Occurrence rate density (events per day in 100 km2, Ml  2.0)

  12. Epidemic model Umbria-marche, 26 September 1997, 00:33 (before the event near Colfiorito, Ml=5.6) Occurrence rate density (events per day in 100 km2, Ml  2.0) 1 6 0 . 0 0 1 5 0 . 0 0 (a) 1 4 0 . 0 0 1E+001 1 3 0 . 0 0 1 1 2 0 . 0 0 0.1 ) m 0.01 k 1 1 0 . 0 0 ( Y 0.001 1 0 0 . 0 0 0.0001 9 0 . 0 0 1E-005 8 0 . 0 0 1E-006 7 0 . 0 0 6 0 . 0 0 -60.00 -40.00 -20.00 0.00 20.00 40.00 X (km)

  13. Epidemic model Umbria-Marche, 26 September 1997, 9:40 (before the second event near Colfiorito, Ml=5.8) Occurrence rate density (events per day in 100 km2, Ml  2.0) 1 6 0 . 0 0 (b) 1 4 0 . 0 0 10 1 0.1 1 2 0 . 0 0 ) m 0.01 k ( Y 0.001 1 0 0 . 0 0 0.0001 1E-005 8 0 . 0 0 1E-006 6 0 . 0 0 -60.00 -40.00 -20.00 0.00 20.00 40.00 X (km)

  14. Epidemic model Umbria-Marche, 14 October 1997 (before the event near Sellano, Ml=5.5) Occurrence rate density (events per day in 100 km2, Ml  2.0) 1 6 0 . 0 0 (c) 1 4 0 . 0 0 10 1 0.1 1 2 0 . 0 0 ) m 0.01 k ( Y 0.001 1 0 0 . 0 0 0.0001 1E-005 8 0 . 0 0 1E-006 6 0 . 0 0 -60.00 -40.00 -20.00 0.00 20.00 40.00 X (km)

  15. Epidemic model Umbria-Marche, 3 April 1998 (before the event near Nocera Umbra, Ml=5.0) Occurrence rate density (events per day in 100 km2, Ml  2.0) 1 6 0 . 0 0 (d) 1 4 0 . 0 0 10 1 1 2 0 . 0 0 0.1 ) m k 0.01 ( Y 0.001 1 0 0 . 0 0 0.0001 1E-005 8 0 . 0 0 1E-006 6 0 . 0 0 -60.00 -40.00 -20.00 0.00 20.00 40.00 X (km)

  16. Modified time dependent model (with physical constraints) • The magnitudedistribution is the same for all the earthquakes (Gutenberg-Richter law) The occurrence rate density is the superposition of a time independent (poissonian) component and that of the triggered seismic activity • The occurrence rate of the triggered events depends exponentially on the magnitude of every preceeding event • The spatialdistribution of triggered events is described by an isotropic function around the epicenter of every previous event • The temporal behaviour of the triggered events is derived from the rate-and-state constitutive law

  17. Rate-and-state model - I (Dieterich, 1994) where R is the occurrence rate density of the induced events R0 is the background rate density Dtis the Coulomb stress change A, s and ta are constitutive parameters

  18. Rate-and-state model - II (Dieterich, 1994) is the stress rate in the area Where

  19. Rate-and-state model - III (Dieterich, 1994) Time dependence of the rate of triggered events for different values of the stress change

  20. Rate-and-state model - IV (Console et al., 2004) The total number of triggered events is proportional to the stress change Dt

  21. Rate-and-state model - V (Console et al., 2004) Dependence of on R0 where b is the parameter of the Gutenberg-Richter law is the seismic moment of an earthquake of magnitude m0 is the stress drop, assumed constant m0 and mmax are the minimum and maximum magnitude, respectively

  22. The rate-and-state model applied to a catalog of earthquakes where Dt0 is the stress change at the center of the fault and d0 is the radius of a fault of magnitude m0

  23. d0 is related to the smallest seismic moment and the smallest magnitude m0 through: and

  24. Modified epidemic model Umbria-Marche, 1 September 1997, 00:00

  25. Modified epidemic model Umbria-marche, 26 September 1997, 00:00 (before the event near Colfiorito, Ml=5.6)

  26. Modified epidemic model Umbria-marche, 26 September 1997, 9:40 (before the secondth event near Colfiorito, Ml 5.8)

  27. Modified epidemic model Umbria-Marche, 14 October 1997 ( before the event near Sellano, Ml 5.5)

  28. Full application of the stress transfer and rate-state model • The Coulomb stress change is computed by the information on the source mechanism of any triggering event (stress drop fixed at 2.5 MPa) • Only one free parameter (As) is necessary in the model • The expected seismicity rate is compared with the real observations of seismic activity

  29. Stress transfer and rate-state model Umbria-Marche, 26 September 1997, 00:00 (before the event near Colfiorito, Ml=5.6) Occurrence rate density (events per day in 1000 km2, Ml  2.0)

  30. Stress transfer and rate-state model Umbria-Marche, 26 September 1997, 9:40 (before the second event near Colfiorito, Ml=5.8) Occurrence rate density (events per day in 1000 km2, Ml  2.0)

  31. Stress transfer and rate-state model Umbria-marche, 14 October 1997 (before the event near Sellano, Ml=5.5) Occurrence rate density (events per day in 1000 km2, Ml  2.0)

  32. Stress transfer and rate-state model Umbria-marche, 3 April 1998 (before the event near Nocera Umbra, Ml=5.0) Occurrence rate density (events per day in 1000 km2, Ml  2.0)

  33. Stress transfer and rate-state model (short term) Stress transfer and rate-state model (long term)

  34. Discussion - I It is possible to observe how the Coulomb stress change affects the spatial and temporal distribution of the seismicity. However, a complete match of the modeled earthquake distribution with the observations is not achievable.

  35. Discussion - II Possible causes of mismatch: • Uncertainties in the source parameters • Stress drop assumed constant • Lack of details on the slip distribution • Uncertainties in the background seismicity • Uncertainties in hypocentral locations • Depth assumed constant • Ignoring previous stress hetereogeneties • Focal mechanism assumed constant • Ignoring the real shape of seismogenic structures • Ignoring the triggering potential of moderate and minor seismicity.

  36. Conclusions Stochastic modeling allows the computation of the expected earthquake rate density on a continuous space-time volume, suitable for the validation of a model with respect to others and for real time forecasts. The significant steps made during the last decades in the physical modeling of earthquake clustering provide a tool for the refinement of these stochastic models. Jointly with the improvement of the seismological observations, these steps appear as a progress towards the possible practical application for earthquake forecast.

  37. Thank you!

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