1. On constraining dynamic parameters from finite-source rupture models of past earthquakes Mathieu Causse (ISTerre) Luis Dalguer (ETHZ) and Martin Mai (KAUST)

2. Source characterization of future earthquakes Statistical analyses Databases of finite-source rupture models Strategy to simulate future earthquakes Source inversion Event 2 Event 1 Event… “Realistic” ground-motion predictions Ground-motion variability

3. FRACTURE ENERGY (G) “Realistic” ground-motion predictions Ground-motion variability Statistical analyses Databases of finite-source rupture models Source inversion Event 2 Event 1 Event… Strategy to simulate future earthquakes

4. Why is fracture energy important? Fundamental parameter in spontaneous dynamic rupture simulations Poorly constrained…

5. Strategy to assess G Input data: 33 kinematic source inversion models from 22 crustal events

6. Strategy to assess G Input data: 33 kinematic source inversion models from 22 crustal events Main stages – 3D-computation of local shear-stress history from local slip velocity (e.g. Mikumo and Miyatake 1995, Tinti et al. 2005)

7. Strategy to assess G

8. Input data: 33 kinematic source inversion models from 22 crustal events Main stages – 3D-computation of local shear-stress history from local slip velocity (e.g. Mikumo and Miyatake 1995, Tinti et al. 2005) – Average fracture energy for individual events

9. Strategy to assess G Input data: 33 kinematic source inversion models from 22 crustal events Main stages – 3D-computation of local shear-stress history from local slip velocity (e.g. Mikumo and Miyatake 1995, Tinti et al. 2005) – Average fracture energy for individual events – Analysis of fracture energy scaling

10. Data processing 1. Static slip interpolation Tottori earthquake, 2000 Semmane et al. 2005 W b =21 MJ/m 2 W b =14 MJ/m 2 W b =17 MJ/m 2 W b =16 MJ/m 2

11. Data processing 1. Static slip interpolation 2. Source-velocity functions are low-pass filtered

12. Do kinematic inversions provide reliable input data? Inversion techniques are affected by numerous uncertainties (e.g. Mai et al. 2007, Beresnev 2003,…) Do different models for the same event provide similar average fracture energy?

14. What are the main kinematic parameters controlling fracture energy? Sensitivity to static slip roughness (or D max /L) Sensitivity to the shape of the source-velocity function Sensitivity to the rise time

15. Sensitivity to static slip roughness Rice et al. (2005) showed that: What are the main kinematic parameters controlling fracture energy?

16. What are the main kinematic parameters controlling W b ? Sensitivity to static slip roughness (or D max /L)

17. What are the main kinematic parameters controlling W b ? Sensitivity to static slip roughness – Fracture energy increases with slip roughness

18. Sensitivity to source velocity function What are the main kinematic parameters controlling fracture energy?

19. Sensitivity to source velocity function – The use of “smooth” source-velocity functions leads to lower fracture energy – The low-pass filtering process tends to underestimate fracture energy BUT it partially removes the discrepancies (homogeneous dataset) What are the main kinematic parameters controlling fracture energy?

20. Sensitivity to rise time – Short rise time leads to higher fracture energy What are the main kinematic parameters controlling fracture energy?

21. Consequently, the robustness of our fracture energy estimates rests on a fair resolution of the slip roughness and the average rise-time What are the main kinematic parameters controlling fracture energy?

22. Scaling of fracture energy Fracture energy is scale dependent: global property

23. Scaling of fracture energy Fracture energy increases with seismic moment Tendency compatible with previous studies Epistemic variability due to uncertainties in kinematic inversion is much smaller than aleatory variability

24. Slip roughness as a further constraint to refine fracture energy empirical relationships? Fracture energy is strongly sensitive to slip roughness (or D max /L) Slip roughness might be linked to the roughness of fault- surface topography, which can be measured on exhumed faults (Candela et al. 2011) Slip roughness might be connected to the notion of fault structural maturity (Manighetti et al. 2007)  Slip roughness is predictable?

25. D max /L and fault structural maturity (Manighetti et al. 2007) Maturity is linked to the fault geometry and long-term fault history (age, cumulative displacement…) Earthquakes break a variable number of segments, increasing with maturity The shape and amplitude of slip profiles vary accordingly 4 functions are proposed to predict D max /L with respect to fault maturity

26. Fracture energy vs. M 0 and D max /L We have split our source model dataset in 2 categories: “Rough” (large D max /L) “Smooth” (low D max /L) Fracture energy is then analyzed separately for the 2 datasets “Rough” slip models (functions 1-2) “Smooth” slip models (functions 3-4)

27. Fracture energy vs. M 0 and D max /L “Rough” models have larger fracture energy (x  5) The scatter is strongly reduced (σ  0.45  σ  0.3)

28. Conclusions We propose new empirical models to constrain fracture energy for future earthquakes that can be used in advanced source modeling for near- source ground-motion simulation It seems that “poorly resolved” kinematic in version models still provide useful input data to analyze the average fracture energy Fault-surface topography measurements and structural maturity analysis might help refining fracture energy assessment to simulate future earthquakes

29. Next steps Stress drop? Radiated energy? Sensitivity of ground-motion to fracture energy? What is really mapped into G: Off-fault damage? Heterogeneity of frictional fault properties?