On modeling induced seismicity Flaminia Catalli with Valentin Gischig Men-Andrin Meier Stefan Wiemer Sebastian Hainzl Torsten Dahm

First Story : earthquake interactions, the Basel case and a stochastic model

A) C) Catalli et al., GRL (2013) D) B)

Geomechanical stochastic seed model Gischig and Wiemer, GJI (2013) Can we reproduce the CI observed behavior? How considering event interactions may improve a PSHA? Non-linear pore-pressure model (COMSOL) Stochastic seed model synthetic catalogues

Goertz-Allmann and Wiemer, Geophys. (2013) Potential earthquakes (seeds) uniformly random distributed over modeling area Differential stress estimate from in-situ stress field Mohr-Coulomb failure criterion assuming θ optimally oriented Local b-value and magnitude EQ interactions Retriggering stress-drop, DCFS and a new stress state are assigned to all seeds Goertz-Allmann and Wiemer, Geophys. (2013) B)

P DCFSint P+DCFSint t=3days t=5days t=7days

injection only injection+interactions

Still an open question DCFSp DCFSint We show that the contribution to the cumulative static stress, caused by the occurrence of small earthquakes, at the site of pending earthquakes is at least as important as the contribution from the largest earthquakes. This is a direct consequence of the fractal clustering properties of earthquake hypocentre distributions. With an isotropic distribution of hypocenters I am possibly loosing the very near-field contribution to cumulative stress

Conclusions #1 : Interaction Coulomb stress changes may improve the spatial assessment of induced seismicity We need a robust null hypothesis to confirm the validity of the CI time-distance behavior Do we maybe need a more realistic hypocenter distribution considering fractal clustering?

Second Story : Rate-and-state effect of pore-pressure diffusion on induced seismicity

Linear pressure diffusion equation

Rate-and-state model no perturbation stressing rate change sudden stress change The RS model provides formulas describing the change of seismicity rate following a specific frictional law, which describes a time-dependent failure process. Toda et al. Nature (2002)

D = 0.05 m2/sec radius of the source = 1 m

Conclusions #2 : Rate-and-state does not have a tuning parameter to fit the total number of events without providing physical reasons Rate-and-state based predictions are very sensitive to the pressure model or to local variations of the background