Auxiliary material for
Slip rates and spatially variable creep on faults of the northern San Andreas system inferred through Bayesian inversion of Global Positioning System Data
Jessica R. Murray 1 , Sarah E. Minson 2 , and Jerry Svarc 1
1
U.S. Geological Survey, Menlo Park, CA, USA
2
Division of Geological and Planetary Science, California Institute of Technology, Pasadena,
CA, USA
Journal of Geophysical Research, Solid Earth
Peer-Review DISCLAIMER: This draft manuscript is distributed solely for purpose of scientific peer review. Its content is merely being considered for publication, so it is not to be disclosed or released by reviewers. Until the manuscript has been approved for publication by the U.S.
Geological Survey (USGS), it does not represent any official USGS finding or policy.
1

Introduction
The auxiliary material provides detailed information about data processing and analysis, observed and predicted data, model geometry parameters, and our estimated creep rates and slip rates. This is contained in one text document, five tables, and two figures (6 figure files in total).
Auxiliary files:
1.
text01.pdf: This file describes the methodology used for processing the raw GPS data, estimating velocities from the position time series, using Bayesian model class selection to infer model fault geometry parameters, and results obtained from a bounded least squares inversion regularized with spatial smoothing.
2.
ts01.txt Table S1: Noise parameters used in estimating velocities for CGPS sites. See
Langbein [2004] for details.
2.1
Column “stat”, station ID
2.2
Column “C”, displacement component (East, North, Up)
2.3
Column “wn”, white noise amplitude (mm)
2.4
Column “PL1 in”, first power law index (1 = flicker; 2 = random walk)
2.5
Column “PL1 A” , first power law amplitude (mm/(yr^(n/4)) where n is power law index
2.6
Column “PL2 in”, second power law index
2.7
Column “PL2 A”, second power law amplitude
2.8
Column “GM tc”, Gauss-Markov time constant (rad/yr)
2.9
Column “BP A”, amplitude of band pass filtered noise (mm)
2.10
Column “BPF L”, lower pass-band limit of the band-pass filtered noise (cycles/year)
2

2.11
Column “BPF U”, upper pass-band limit of the band-pass filtered noise (cycles/year)
2.12
Column “P”, number of poles in band pass filter
3.
ts02.txt Table S2: Site information. observed, and predicted velocities for continuous
GPS sites
3.1
Column “stat”, station ID
3.2
Column “lon”, longitude (degrees east)
3.3
Column “lat”, latitude (degrees north)
3.4
Column “yr1”, first year of observations
3.5
Column “yr2”, last year of observations
3.6
Column “nobs”, number of data points in time series
3.7
Column “vel E”, east velocity (mm/yr)
3.8
Column “vel N”, north velocity (mm/yr)
3.9
Column “vel V”, vertical velocity (mm/yr)
3.10
Column “unc E”, east velocity uncertainty (1 sigma, mm/yr)
3.11
Column “unc N”, north velocity uncertainty (1 sigma, mm/yr)
3.12
Column “unc V”, vertical velocity uncertainty (1 sigma, mm/yr)
3.13
Column “pred E”, predicted east velocity (mm/yr)
3.14
Column “pred N”, predicted north velocity (mm/yr)
3.15
Column “pred V”, predicted vertical velocity (mm/yr)
3

4.
ts03.txt Table S3: Site information, observed, and predicted velocities for campaign GPS sites.
4.1
Column “stat”, site ID
4.2
Column “lon”, longitude (degrees east)
4.3
Column “lat”, latitude (degrees north)
4.4
Column “yr1”, first year of observations
4.5
Column “yr2”, last year of observations
4.6
Column “nyrs”, number of years with surveys
4.7
Column “nobs”, number of data points in time series
4.8
Column “vel E”, east velocity (mm/yr)
4.9
Column “vel N”, north velocity (mm/yr)
4.10
Column “vel V”, vertical velocity (mm/yr)
4.11
Column “unc E”, east velocity uncertainty (1 sigma, mm/yr)
4.12
Column “unc N”, north velocity uncertainty (1 sigma, mm/yr)
4.13
Column “unc V”, vertical velocity uncertainty (1 sigma, mm/yr)
4.14
Column “pred E”, predicted east velocity (mm/yr)
4.15
Column “pred N”, predicted north velocity (mm/yr)
4.16
Column “pred V”, predicted vertical velocity (mm/yr)
4

5.
ts04.pdf Table S4: Bayesian model class selection to estimate SAF locking depth and dips of MF and BSF creeping zones
5.1
Column “Candidate locking depths”, locking depths tested in analysis (km, 1 km increments); MF and BSF locking depths held fixed at 9 km and 13 km, respectively.
5.2
Column “Candidate dips”, dips of the creeping zone tested in analysis (degrees NE, 5 o increments); SAF dip held fixed at 90 degrees; dip applies to portion of fault above locking depth.
5.3
Column “Most likely locking depth”, most likely locking depth based on maximum marginal likelihood (km)
5.4
Column “Most likely dip”, most likely dip for creeping zones based on maximum marginal likelihood (degrees NE)
5.5
Column “Best fitting deep slip rate”, best fitting deep slip rate estimated using Bayesian linear regression for most likely model class (mm/yr)
6.
ts05.pdf Table S5 Bayesian model class selection to estimate locking depths and dips of
MF and BSF creeping zones
6.1
Column “Candidate locking depths”, locking depths tested in analysis (km, 1 km increments)
6.2
Column “Candidate dips”, dips of the creeping zone tested in analysis (degrees NE, 5 o increments); SAF dip held fixed at 90 degrees; dip applies to portion of fault above locking depth.
6.3
Column “Most likely locking depth”, most likely locking depth based on maximum marginal likelihood (km)
5

6.4
Column “Most likely dip”, most likely dip for creeping zones based on maximum marginal likelihood (degrees NE)
6.5
Column “Best fitting deep slip rate”, best fitting deep slip rate estimated using Bayesian linear regression for most likely model class (mm/yr)
7.
ts06.txt Table S6: Model dislocation geometry used in Bayesian inversion (Figure 5b).
Origin of local coordinate system is at -122.9608E, 39.4536N, 564.0746 meters.
7.1
Column “L”, length in along strike direction (km)
7.2
Column “W”, width in down-dip direction (km)
7.3
Column “D”, depth to center of bottom edge of dislocation (km)
7.4
Column “dip”, dip measured from Earth's surface downward in a direction 90 degrees clockwise from strike direction (degrees)
7.5
Column “strike”, strike measured clockwise from north (degrees)
7.6
Column “east”, east coordinate of midpoint of bottom edge of dislocation in local
Cartesian coordinate system (km)
7.7
Column “north”, north coordinate of midpoint of bottom edge of dislocation in local
Cartesian coordinate system (km)
7.8
Column “sf”, subfault index referenced in Table S5
8.
ts07.txt Table S7: Creep rates and deep slip rates estimated using Bayesian inversion.
Estimates are derived from posterior probability distribution and are mapped to subfaults.
8.1
Column “mode”, mode of posterior distribution (mm/yr)
8.2
Column “mean”, mean of posterior distribution (mm/yr)
6

8.3
Column “med”, median of posterior distribution (mm/yr)
8.4
Column “std”, standard deviation of posterior distribution (mm/yr)
8.5
Column “sf”, subfault index (corresponds to Table S4 and Figure 5b)
8.6
Column “gf”, Green’s function index (corresponds to Figure 7 and Figure S1)
9.
fs01.pdf
Figure S1: Spatially variable creep rate and uniform deep slip rates estimated using bounded least squares regularized with spatial smoothing.
10.
fs02a.pdf, fs02b.pdf, fs02c.pdf, fs02d.pdf
Figure S2: Posterior distributions and tradeoffs among creep rate and deep slip rate estimates. Each scatter plot shows the tradeoff between the estimates whose Green’s function index is given by the corresponding row and column headers. Locations of
Green’s functions on the fault are depicted in the diagram. Axis limits are the same for all panels in a given subfigure and are shown on lower left scatter plot and histogram.
Units are mm/yr. a) MF strike-slip; b) MF dip-slip; c) BSF strike-slip; d) BSF dip-slip.
11.
fs03a.pdf, fs03b.pdf
Figure S3: Tradeoff among strike-slip and dip-slip rate estimates for each fault. Axis limits indicated in upper left panel apply to all panels on that subfigure. Green’s function indices refer to diagrams in Figure S1. a) MF; b) BSF.
7