Slow Slip and Tremor Along San Andreas fault system

Slow Slip and Tremor Along San Andreas fault system Slow slip in Upper Crust Aseismic creep, afterslip and spontaneous slow slip events on some faults in upper 15 km Mostly aseismic but accompanied by small, repeating micro-earthquakes Responds to stress changes Can be imaged with geodesy/seismology to constrain frictional fault models Slow slip in Lower Crust Interseismic and postseismic slip or more distributed shear in lower crust Tremor, composed of LFE near Parkfield, and probably other places Tremor extremely sensitive to very small (shear) stress changes (tidal, teleseismic, regional) Details can be imaged seismologically

Example: Hayward Fault Fremont creep array, Lienkaemper et al. Green triangles are creepmeter/aa arrays locations NW SE Johanson, 2009

Example: SAF from SJB to PKF Johanson et al., 2006 Johanson & Bürgmann, 2005 1966-2004 Interseismic 2004 Coseismic Murray & Langbein, 2006 Ryder et al., 2008

Slow Slip and Tremor Along San Andreas fault system Slow slip in Upper Crust Aseismic creep, afterslip and spontaneous slow slip events on some faults in upper 15 km Mostly aseismic but accompanied by small, repeating micro-earthquakes Responds to stress changes Slow slip in Lower Crust Interseismic and postseismic slip or more distributed shear in lower crust Tremor, composed of LFE near Parkfield, and probably other places Tremor extremely sensitive to very small (shear) stress changes (tidal, teleseismic, regional) Shelly, 2009 & 2010 Shelly, 2009 GRL

Tremor is Easily Triggered Nadeau & Guilhem, 2009 Science 2002 Denaliearthquake triggered tremor

Tremor Triggered by Right-lateral Shear Stress 24 December, 2009 Tremors, but not earthquakes are easily triggered by tidal stress cycles Right-lateral shear stress parallel to San Andreas maximizes triggering Almost no normal-stress triggering low friction µ = 0.02

Friction and Effective Stress are Very Low Tremor Micro-earthquakes Overburden > 600 MPa Tremor is triggered by very small, 100 Pa tidal shear stress Deep fault zone is weak and at ~lithostatic fluid pressure!

Triggering is Spatially Heterogeneous Monarch Peak Parkfield Cholame Overburden > 600 MPa Thomas, Shelly & Bürgmann, 2010 SSA Variable tidal triggering of tremor with depth and along fault

Slow Slip and Tremor Along San Andreas fault system Slow slip in Upper Crust Aseismic creep, afterslip and spontaneous slow slip events on some faults in upper 15 km Mostly aseismic but accompanied by small, repeating micro-earthquakes Responds to stress changes Can be imaged with geodesy/seismology to constrain frictional fault models Slow slip in Lower Crust Interseismic and postseismic slip or more distributed shear in lower crust Tremor, composed of LFE near Parkfield, and probably other places Tremor extremely sensitive to very small (shear) stress changes (tidal, teleseismic, regional) Details can be imaged seismologically

Finite Fault Simulations of Transient Creep Best fitting springslider result for various fixed d c Shear stress change from Loma Prieta earthquake drops creep rate over entire modeled fault (first panel) Creep returns to steady-state rate by 2007 using friction parameters from the spring-slider inversion Frictional parameters can be used to model aftermath of large Hayward ruptures

Example: SAF from SJB to PKF Chen et al., 2010 in prep. Nadeau & McEvilly, 2004

Rate-State Frictional Afterslip coseismic slip high low shear stress change increase decrease Specify smoothing parameter Invert coseismic offsets for slip Specify fault rheological parameters (rate-state friction) high low afterslip Johnson et al., 2006, BSSA

Rate-State Frictional Afterslip

Rate-State Frictional Afterslip parameter 95% bounds coseismic slip a b a-b D c 0. 01 (fix) 0.0-0.009 0.001-0.01 0.015-0.3 m The estimate of a-b values falls within range of experimental values reported for serpentinite 0 10 20 30 40 50 60 Distance along fault (km) slip (m) 0.3 0.2 0.1 0 afterslip 0 10 20 30 40 50 60 Distance along fault (km) Johnson et al., 2006, BSSA

On average there isn t strong evidence for tidal triggering of this sequence, but the %-excess values fluctuate strongly from negative to positive correlation. Is this significant, does it correlate in any way to the temporal oscillatory behavior? At least from a visual comparison, it s not obvious that there is Are 3-day, 6-day or non-periodic Tr events more or less tidally correlated than the others? There are other sequences, e.g., 19165 that show even more extreme %-excess value fluctuations. This could just be noise, but I wonder if those fluctuations correlate with other aspects of the sequence behavior. Day 1 is 8/30/2001 so Parkfield should be 1121, San Simeon 844 15841 (n = 2032) FNS = 0.9734 1.0112 0.9710 1.0516 RLSS = 0.9585 0.9700 1.0624 1.0044 CS = 0.9811 0.9610 1.0060 1.0690 This is for a 120 day window CENTERED on the respective date. These are relative to RLSS only (so the expected values are computed using the values of the tidally induced RLSS).

On average there isn t strong evidence for tidal triggering of this sequence, but the %-excess values fluctuate strongly from negative to positive correlation. Is this significant, does it correlate in any way to the temporal oscillatory behavior? At least from a visual comparison, it s not obvious that there is Are 3-day, 6-day or non-periodic Tr events more or less tidally correlated than the others? There are other sequences, e.g., 19165 that show even more extreme %-excess value fluctuations. This could just be noise, but I wonder if those fluctuations correlate with other aspects of the sequence behavior. Day 1 is 8/30/2001 so Parkfield should be 1121, San Simeon 844 15841 (n = 2032) FNS = 0.9734 1.0112 0.9710 1.0516 RLSS = 0.9585 0.9700 1.0624 1.0044 CS = 0.9811 0.9610 1.0060 1.0690 This is for a 120 day window CENTERED on the respective date. These are relative to RLSS only (so the expected values are computed using the values of the tidally induced RLSS).