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1 Geophysical Journal International Geophys. J. Int. (2014) 198, GJI Gravity, geodesy and tides doi: /gji/ggu208 Analyzing slip events along the Cascadia margin using an improved subdaily GPS analysis strategy Yuval Reuveni, 1,2 Sharon Kedar, 1 Angelyn Moore 1 and Frank Webb 1 1 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA. yreuveni@ucsd.edu 2 Currently at Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, UC San Diego, La-Jolla, CA 92093,USA Accepted 2014 May 29. Received 2014 May 28; in original form 2013 December 9 SUMMARY A GPS analysis strategy that reduces the noise level of GPS-based subdaily strain measurements and improves subdaily resolution of positions enables the use of baseline estimates in the study of slow slip events along the northern Cascadia margin. We first evaluate this strategy s performance through comparisons of strain estimates at co-located GPS stations and borehole strainmetres, and then examine the strain migration during the 2008 May episodic tremor and slip (ETS) event. The temporal evolution of the ETS event is extracted from the GPS baseline analysis of the bidirectional propagation of the 2008 May event. These results establish the strain estimates from subdaily GPS baseline measurements as a reliable technique that can be used for mapping regional strain variations during episodic slip in regions where no laser or borehole strainmetres exist and GPS stations are abundant. Key words: Satellite geodesy; Transient deformation. 1 INTRODUCTION One of the foremost scientific objectives of regional GPS-based geodetic measurements is the study of strain fields resulting from active deformations across plate boundary zones. Observations of aseismic strain accumulation are key to advancing our understanding of the earthquake cycle, a major goal for GPS networks, such as the Southern California Integrated GPS Network (SCIGN; Hudnut et al. 2002), the plate boundary observatory (PBO; Silver et al. 1999) and GEONET in Japan (Sagiya 2004). Over the past decade, such networks have accumulated enough data to enable analysis of aseismic strain buildup in tectonically active areas. GPS networks have typically been used to look at long-term interseismic strain, with the strain fields derived from displacement measurements or velocity fields that average observation over several years (e.g. Kreemer et al. 2003; Tapeet al. 2009). More recently, transient deformations on the scale of 1 2 weeks accompanied by low amplitude tremor have been observed in the northern Cascadia area, northern Washington (Dragert et al. 2001; Melbourne & Webb 2003; Melbourne et al. 2005; Brudzinski & Allen 2007; Kao et al. 2009), the western Shikoku area, southwest Japan (Obara et al. 2004) and other subduction zones in the circum-pacific region (Schwartz & Rokosky 2007).Motivatedbythediscoveryofthese shorter term transient signals and with recent improvements in GPS analysis strategies and software, researchers are starting to explore how the strain field evolves at shorter timescales with GPS measurements. Since strain is a spatial derivative, its measurement accuracy is dependent upon the accuracy of baseline length estimates between two points on the surface of the Earth (Jaeger & Cook 1969). With networks of over a thousand GPS stations in the US, and a similar number in Japan, comprising numerous baselines of multiple length scales, current GPS networks have the spatial density to become powerful tools for subdaily strain monitoring. Recently, it has been shown that subdaily strain estimates, derived from GPS baselines measurements, can be substantially improved by using a noise reduction strategy which minimizes the key sources of error due to diurnal effects from path delays caused by refractions of the GPS signal near the receiver (multipath), and refractions from tropospheric delays, by a factor of 5 or more (Reuveni et al. 2012). This is accomplished by applying tropospheric corrections derived from pre-estimated static station positioning calculations, and by applying a modified sidereal filter (MSF) correction to the phase data prior to performing a kinematic estimation of the station positions (e.g. Choi et al. 2004; Reuveniet al. 2012). Since this noise reduction strategy enables a new capability for routinely measuring tectonic strain using GPS and offers observations of the deformation field in the relatively underexplored period band of 1 hr to a day,it can serve as an exploratory tool for mapping regional variations in subdaily strain in regions where few laser or borehole strainmetres exist and GPS stations are abundant. Borehole strain metres (BSM) and laser strain metres (LSM) typically measure subdaily strain with 2 3 orders of magnitude better accuracy than GPS derived strain (Reuveni et al. 2012), buttheir costand demanding sitingand installation requirements (Agnew & Wyatt 2003; Roeloffs 2004, 2005) result in reduced geographic extent and numbers compared with the much greater number and distribution of GPS stations. When the C The Authors Published by Oxford University Press on behalf of The Royal Astronomical Society. 1269

2 1270 Y. Reuveni et al. Figure 1. Distribution of BSM sites (white circles with red outline) and GPS sites (black squares) along the northern Cascadia margin that were used to study the 2008 May slow slip event. Two sets of BSM and GPS co-located station were found, B928 and baselines BAMF-NTKA and JORT-BAMF, along with one BSM station along the baseline, B012 and baseline BAMF-NTKA. The diverging arrows (yellow) indicate the bidirectional propagation. Further information regarding the baselines locations can be found in Fig. A1. Depth contours of the plate interface from McCrory et al. (2012) are shown in white dash lines. The slip model illustration from Dragert & Wang (2011) is also shown in 1 cm contours. strain signal is large enough to be measured by GPS and there is a GPS network with suitable geographic distribution, as there is for the Cascadia episodic tremor and slip events, GPS derived strain has the potential to provide information about the geographic evolution of the strain transient down to subdaily periods. In order to ascertain whether the strain field extracted from GPS baseline estimates is consistent with the more accurate point strain measurements made by BSM, we examine the along-strike strain migration during episodic slip and tremor for a given location in the northern Cascadia 2008 May event (Wang et al. 2008; Wech et al. 2009). Northern Cascadia (Fig. 1) is an ideal location for studying along-strike propagation of slow slip since individual episodic tremor and slip (ETS) events tend to span km along strike (Rogers & Dragert 2003; Dragert& Wang 2011). We carry out this analysis by focusing on the 2008 May ETS event, which provides exceptionally detailed geodetic and seismic information due to intense instrumental development during that period (Wech et al. 2009; Houston et al. 2011). Once the validity of the GPS strain measurement is established, it is applied to region without BSM coverage. 2 OBSERVATION OF THE 2008 MAY ETS EVENT 2.1 Noise reduction for GPS data For the 2008 May ETS event, a total of 13 GPS sites were analysed (Fig. 1). We used the GPS analysis strategy described in detail by Reuveni et al. (2012), to improve the subdaily noise levels of station positions and the baseline strain estimates. The results of the noise reduction GPS data analysis strategies were obtained with the Jet Propulsion Laboratory s (JPL s) GIPSY-OASIS precise point positioning software and products (Zumberge et al. 1997; Bertiger et al. 2010). After testing the data with the sidereal filter at 30 s, 5 min and 30 min resolution, we concluded that the noise levels were too high to detect any apparent signal. Therefore, the data were processed as sidereally filtered 4-hr kinematic station positions, fixing the tropospheric parameters at every epoch to values estimated in static position calculations. The GPS ground stations position parameters were modelled as random walk stochastic process, which were allowed to vary within 5.7e 7 km s 1 (corresponding to about 1.7 mm in an hour). We used the global mapping function as

3 Analyzing slip events along the Cascadia margin 1271 mapping function (Boehm et al. 2006b), and a 7 minimum elevation cut-off for the satellite observations. The troposphere wet delay and gradient (tilt direction of the mapping function) parameters were modelled as random walk stochastic processes, whereas the tropospheric dry delay parameters remain fixed. The MSF corrections were determined by time shifting (calculated from the JPL precise orbits for the target day and for the specific satellite) and then stacking the 30 s satellite-specific phase residuals at a given station over ±5 d before and after the target day by the orbit repeat period of each satellite. The stacked residual values were then interpolated using a 4th-order polynomial and applied as a correction to the phase and pseudorange observations of the target day. The filtered postfit residuals, along with the pre-estimated tropospheric delays, formed the input for the subdaily point positions and consequently the baseline strain estimates. For a more detailed description of the methodology, as well as the exact values for tropospheric parameters, see Reuveni et al. (2012). 2.2 Baseline strain estimates To calculate the GPS strain we first calculate the mean baseline Euclidean length between GPS station pairs (for example, BAMF- NTKA in Fig. 1) over approximately 3 months of positioning data spanning the ETS episode, which is then subtracted from the baseline measurement at each epoch. The remainder (i.e. the variation about the mean) is then divided by the baseline mean length to calculate the linear strain variation along the vector connecting the two stations. After extracting the baseline strain estimates, the data were then smoothed using Matlab s curve fitting toolbox quadratic fit (with a span equal to , i.e per cent, which can be translated into 20 hr of data point smoothing, resulting in effective subdaily time-series resolution) in order to capture any important strain event in the data, while filtering out unmodelled noise or other shorter period phenomena. The Matlab quadratic fit smoothing process is a locally weighted scatter plot smoother, as each smoothed value is determined by neighbouring data points using a regression weight function defined within the span (see _6ys3-3). In addition to the regression weight function, we also used the robust weight function option, which makes the process resistant to outliers. 2.3 Projected borehole data For the BSM data (locations are shown in Fig. 1 as white circles), we used level 2 processed strain data (the areal strain ɛ EE + ɛ NN, and the shear strain component γ 1 = ɛ EE ɛ NN,andγ 2 = 2ɛ NE,whereɛ EE is the east west normal strain, and ɛ NN is the north south normal strain, and the shear strain ɛ NE = ɛ EN ), corrected for their respective healing trends as well as their tidal and atmospheric corrections, obtained from the PBO archives (Anderson et al. 2006). The time span was chosen to precisely overlap the analysed GPS data and was down sampled to a 4-hr sampling rate to match the GPS timeseries. After removing the formal healing trends and applying the tidal and atmospheric corrections (which are also provided as part of PBO level 2 processed strain data), remaining long-period nonlinear variations or trends in the residual time-series were removed by subtracting low-order polynomials fitted to the residual timeseries. We maintained the polynomials order to 5 or lower while resolving it based on the lowest order that followed approximately zero-slope levels prior to and after the ETS transient strain signal, Figure 2. BSM projected strain (black line), along with the areal (red line) and shear (blue and grey lines) components after removing the formal healing trends and applying the tidal and atmospheric corrections. BSM B928 is projected on to baseline BAMF-NTKA (upper panel), BSM B012 is projected on to baseline BAMF-NTKA (middle panel), and BSM B928 is projected on to baseline JORT-BAMF. in a similar manner as reported by Dragert & Wang (2011). We then also smoothed the resultant time-series using a quadratic fit with the same span that was used for the sidereally filtered GPS timeseries data. Furthermore, in order to adequately compare the strain values obtained from the baseline estimates to those measured by the BSM, we projected the measured BSM strain onto the baseline direction by rotating the full strain tensor into the azimuth of the GPS baseline. This procedure can be describe as follows: ε Projected = ε EE + ε NN 2 where ε EE ε NN 2 cos 2θ ε NE sin 2θ, (1) θ = 180 α(α = baseline azimuth). (2) An example for the BSM projected strain, along with the areal and shear components, is presented in Fig. 2. The data are plotted for the BSM stations that were used to validate the GPS baseline strain estimates, that is BSM B928 is projected on to baseline BAMF- NTKA (upper panel), BSM B012 is also projected on to baseline BAMF-NTKA (middle panel) and BSM B928 is projected on to baseline JORT-BAMF (lower panel). 3 RESULTS 3.1 Validation: comparison between co-located GPS and borehole strain We validated the GPS baseline strain estimates at two BSM stations that are co-located with one of the GPS stations spanning the baseline, and one BSM station along the baseline. The first is B928 and baseline BAMF-NTKA (Fig. 3 upper panel), the second is B012 and baseline BAMF-NTKA (Fig. 3 middle panel), and the third is B928 and baseline JORT-BAMF (Fig. 3 lower panel). The baselines length ranges from 91.2 km (JORT-BAMF) to km (BAMF- NTKA). The co-located GPS station locations are also presented in Fig. A1, as segment A, and B + C, corresponds to the baselines BAMF-NTKA and JORT-BAMF, respectively. Since BSM are sensitive to many tectonic and non-tectonic processes, not all of which are well known, and their associated signals

4 1272 Y. Reuveni et al. Figure 3. Strain estimated from subdaily GPS baselines (blue colour) compared to BSM projected values (red colour) and GPS daily position estimates (black dots) for two co-located cases and one along the baseline. (a) BSM B928 and baseline BAMF-NTKA, a scaling factor equal to 0.5 was applied to the BSM time-series for visualization, and the baseline was shifted backward by 9 d with respect to the slip propagation rate (right-hand side). (b) B012 and baseline BAMF-NTKA, the baseline was shifted backward by 9 d (right-hand side). (c) BSM B928 and baseline JORT-BAMF, the baseline was shifted backward by 11 d (right-hand side). The results show good correlation (0.680, and 0.736, for the three cases, respectively) for the strain pattern between the two methods after applying the time shifting with respect to the slip migration rates obtained by Dragert & Wang (2011). add noise that cannot always be removed by detrending and filtering (Dragert & Wang 2011), the most valuable information in the BSM strain data is the timing of the sudden change due to the onset of slow slip and the sign of that change (increase or decrease). Therefore, these are the characteristics that are of most interest when we come to compare the two methods. However, since the baseline strain measurement is expressed as an integration of the strain change at each point along the baseline between two GPS stations, the strain in comparison to BSM point measurement is expected to be somewhat different, both in amplitude and timing, due to the discrepancy between the two different measurement types. Therefore, in order to adequately compare the two methods one has to take into account the time it takes the strain signal to propagate along the baseline length, as well as the relative position of the BSM station along the baseline. Furthermore, if we assume the fault slips at a constant rate, then the strain signal that is expected to be observed along the baseline compared with the one observed at a point, such as a BSM, can be simply described as the convolution between the projected BSM signal and a boxcar function with a width equal to the time it takes the projected velocity slip vector to propagate along the baseline length (Fig. 4a). Taking B928 and the baseline BAMF-NTKA, which can be assumed to be parallel to the northwest slip propagation, we produced such a pulse shape (Fig. 4b), which illustrates the fact that a widening effect will be present when the strain is integrated along the baseline length. However, to better understand and fully characterize how the strain signal observed along the baseline should look like, it would require a full geodetic modelling of the slow rupture. For our comparison we used the same values for the spatially variable strain propagation rates as presented by Schmidt & Gao (2010) and Dragert & Wang (2011). Fig. A3 is the distance-time function presented by Dragert & Wang (2011), where the values which we used correlate to the red lines in the figure. The time shifts applied to the GPS strain observation correspond to the estimated time for the strain signal to propagate over the GPS baseline length, adjusted by the slip propagation time between the GPS site that sees the slip first and the BSM location if the instruments are not collocated. The results show good correlation between the strain time-series obtained independently by the two methods for the strain pattern (increase or decrease), when applying the evaluated time shifts. When baseline BAMF-NTKA is shifted backward by 9 d (15 km d 1 along km), the correlation coefficient between BAMF-NTKA and B928 is (Fig. 3a right-hand side). When site B012 is not scaled, andbaselinebamf-ntkaisshiftedbackwardby7d(15kmd 1 along km, adjusted by the distance from BAMF to B012, 31.3 km), the correlation coefficient between BAMF-NTKA and B012 is (Fig. 3b right-hand side). When baseline JORT-BAMF is shiftedforwardby11d(8kmd 1 along 91.2 km), the correlation coefficient between JORT-BAMF and B928 is (Fig. 3c right-hand side). The subdaily estimates (plotted in blue in Fig. 3) follow the strain signal, as captured by the BSM, more accurately than GPS daily position estimates (plotted as black dots in Fig. 3). Using the same scaling and time shifting for the GPS daily position estimates, the correlation coefficients for the entire time period are 0.637, and 0.628, for B928 and baseline BAMF-NTKA, B012 and baseline BAMF-NTKA and B928 and baseline JORT-BAMF, respectively. The agreement between the calculated GPS baseline strain and the BSM measured strain demonstrates that we can reliably detect strain signals during episodic slip using GPS baselines with subdaily positions. It follows that GPS can be used as a reliable method for analyzing subdaily temporal and spatial strain variations during ETS events, and perhaps other strain events of similar amplitude in areas where no reliable borehole strainmetres exist. 3.2 Strain migration using GPS baseline estimates The temporal evolution of the ETS slow slip event can also be extracted from the subdaily GPS baseline analysis, as the strain migration is clearly shown for the bidirectional propagation of the 2008 event (Fig. 5), where SC03-P403, P403-JORT, JORT-PTRF, PTRF-BAMF and BAMF-NTKA baselines (Fig. A1, segment E, D, C, B and A, respectively) capture the northwest along-strike propagation, while SC03-P418, P418-P430 and P417-P409 baselines (Fig. A1, segment F, G, H and I, respectively) capture the south propagation. Furthermore, the timing of the observed slow slip at each baseline, as it propagates, is in good agreement with the results obtained by Dragert & Wang (2011) (red lines in Fig. A3) only for the south propagation, while the northwest propagation rates have similar values, but they are slightly shifted in time. In order to estimate the strain propagation rate, the beginning and end of the slow slip event at each baseline was found from the local maximum and minimum peak strain values (indicated by the red points in Fig. 5), along with the mid-point values (indicated by the red points with black outline, located on the dashed red lines connecting the local maximum and minimum peak strain values). The local maximum and minimum peak strain values for each baseline were calculated within a locally 32 d time window, which is the maximum duration for the entire regional slip event that is defined from observed heightened tremor activity (Dragert & Wang 2011), while taking into account the tremor locations (Wech et al. 2009; Houston et al. 2011). For each pair of baselines we then calculated the time difference between the average values (indicated as T in

5 Analyzing slip events along the Cascadia margin 1273 Figure 4. (a) Illustration of the strain signal that is expected to be observed along the baseline as the product of the convolution between the projected BSM signal and a boxcar function with a width equal to the time it takes the projected velocity slip vector to propagate along the baseline length. (b) The pulse shape product (red line) is plotted along with the observed baseline strain BAMF-NTKA (blue line), and BSM B928 (black line). The widening effect appears as the strain is integrated along the baseline length. Fig. 5), along with the baselines relative distance. For example, the local maximum and minimum peak values for baseline P418-P430 occur at days ± 0.8 and ± 0.8, respectively, while the average value is day ± 0.8 (the error values are extracted from the smoothed GPS baselines time-series resolution which is equal to 20 hr, or 5/6 of a day, 0.8). In a similar way, the mid-point value for baseline P417-P409 is ± 0.8. The time difference ( T) between the two mid-point values is 16.7 ± 1.7 d, while the relative distance between the two baselines is km, and so we estimate the strain migration velocity to be 6.1 ± 0.6 km d 1. Calculating the mid-point values average the spatially integrated effect (which depends on the length and orientation of the baseline, and the relative geometry of the baseline with respect to the evolving slip surface) as the baselines change in length. This actually helps us convert the linear GPS baseline-measured strain time-series (which are subject to independent motions at the baseline ends, and therefore an onset of strain variations cannot be spatially ascribed to a precise location along the baseline), to a point measurement when we set to evaluate the strain migration rate. From our analysis we estimate that the strain signal starts between day ± 0.8 (SC03- P403 northwest propagation) and ± 0.8 (SC03-P418 southwest propagation) of 2008 in the vicinity of GPS station SC03, and propagates bidirectionally. The northwest piecewise linear propagation is found to be approximately 6.3 ± 1.4 km d 1 from day

6 1274 Y. Reuveni et al. along the plate interface) are calculated from the arrival time difference of a strain change between two baselines on the surface above the plate interface. This is opposed to Dragert & Wang (2011), which modelled a fault slip pattern for the slip rate estimation that occurs on the fault and not above it. Furthermore, when our baseline estimates are averaged over the same period as evaluated by Dragert & Wang (2011) the agreement between the two methods is further improved. Although BSM has better temporal resolution, the temporal resolution of these rates could be further improved as more baselines are added to form a denser spatial coverage, which could reveal important information about the mechanical relation between slow slip and tremor (Dragert & Wang 2011). Figure Strain migration as captured using GPS sidereal filtered baselines. The bidirectional propagation is also shown from the eight different baselines (blue colour). The local (32 d time window indicated by the shaded grey boxes, approximated by tremors activity) maximum and minimum peak strain values (red dots), along with the mid-point value (indicated by the red points with black outline, located on the dash red lines connecting the local maximum and minimum peak strain values), were used to calculate the time difference ( T) between each two mid-point values. The strain propagation rate estimations (black arrows) were then calculated from the baselines relative distance and time difference ( T) ± 0.8 to ± 0.8, then increases to 10.2 ± 2.4 km d 1 until day ± 0.8. From there, the strain slows down to 2.2 ± 0.3 km d 1 until day ± 0.8, and increases back again to 15.3 ± 2.3 km d 1 around day ± 0.8. The southwest linear propagation is estimated to be 5.1 ± 0.8 km d 1 between day ± 0.8 and ± 0.8, and then increases to 6.1 ± 0.6 km d 1 until day ± DISCUSSION Dragert & Wang (2011) evaluated (Fig. A3, red lines) that the slip started on day 125 in the area of GPS site SC03 and propagated to the northwest direction at initial speed of 8 km d 1, then slowed down to 2 km d 1 at day 140, and sped up to 15.5 km d 1 at day 148. For the southwest propagation, the estimation was a uniform rate of 6 km d 1. The estimated strain rate obtained from our baseline analysis shows a similar pattern to what was determined for this event by Schmidt & Gao (2010) and Dragert & Wang (2011), for the southwest propagation and most of the time periods for the northwest propagation. The observed difference during some periods between the strain rate obtained from our analysis, and the slip rate evaluated by Dragert & Wang (2011), is probably due to the fact that the strain migration velocities (caused by the slip migration 5 CONCLUSIONS Using the method described by Reuveni et al. (2012) and Choi et al. (2004) that significantly reduces subdaily GPS station position noise and consequently the baseline noise caused by multipath and subdaily tropospheric variations, we compare the regional subdaily strain estimated from GPS measurements, with local BSM data, along the northern Cascadia region during a 2008 ETS event. The results show good agreement between the two methods in the strain pattern, and at several cases also in time of occurrence. The bidirectional slip propagation identified by Dragert & Wang (2011) is also reliably captured by GPS strain measurements, with GPS strain measurements showing some differences and greater temporal resolution for the northwest propagation rates compared with those identified by Dragert & Wang (2011). This result shows that by employing the tropospheric and the MSF reduction strategies, modern regional GPS networks can function as a valuable exploratory tool for mapping regional variations in subdaily strain related to slow slip in regions where no borehole strainmetres exist, or to enhance borehole strainmetre measurements, where GPS stations are abundant. ACKNOWLEDGEMENTS The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. C 2012 California Institute of Technology. Government sponsorship acknowledged. We would like to thank Dr Susan Owen from Jet Propulsion Laboratory, California Institute of Technology, for her suggestions and comments. We would also like to thank Dr Herb Dragert from the Pacific Geoscience Center, Geological Survey of Canada, for his assistance with GPS data. BSM data were obtained from the PBO archives at UNAVCO-ftp://bsm.unavco.org/pub/bsm/level2/. REFERENCES Agnew, D.C. & Wyatt, F.K., Long-base laser strainmeters: a review, Scripps Institution of Oceanography Technical Report, 6 January. Anderson, G., Hodgkinson, K., Herring, T. & Agnew, D.C., Plate boundary observatory data management system critical design review version 1.2. Available at: literature /gji/ggu208.html, accessed September 2012 July Bertiger, W., Desai, S.D., Haines, B., Harvey, N., Moore, A.W., Owen, S. & Weiss, J.P., Single receiver phase ambiguity resolution with GPS data, J. Geod., 84(5), Boehm, J., Niell, A., Tregoning, P. & Schuh, H., 2006b. 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7 numerical weather model data, Geophys. Res. Lett., 33, L07304, doi: /2005gl Brudzinski, M. & Allen, R.M., Segmentation in episodic tremor and slip all along Cascadia, Geology, 35(10), Choi, K., Bilich, A., Larson, K.M. & Axelrad, P., Modified sidereal filtering: implications for high-rate GPS positioning, Geophys. Res. Lett., 31, L22608, doi: /2004gl Dragert, H., Wang, K. & James, T.S., A silent slip event on the deeper Cascadia subduction interface, Science, 292, Dragert, H. & Wang, K., Temporal evolution of an episodic tremor and slip event along the northern Cascadia margin, J. geophys. Res., 116, B12406, doi: /2011jb Hudnut, K.W., Bock, Y., Galetzka, J.E., Webb, F.H. & Young, W.H., The Southern California integrated GPS network (SCIGN), in Seismotectonics in Convergent Plate Boundary, pp , eds Fujinawa, Y. & Yoshida, A., TERRAPUB. Houston, H., Delbridge, B.G., Wech, A.G. & Creager, K.C., Rapid tremor reversals in Cascadia generated by a weakened plate interface, Nat. 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Res. Lett., 31, L23602, doi: /2004gl Reuveni, Y., Kedar, S., Owen, S.E., Moore, A.W. & Webb, F.H., Improving sub-daily strain estimates using GPS measurements, Geophys. Res. Lett., 39, L11311, doi: /2012gl Roeloffs, E.A., Low-frequency borehole strain monitoring in Northern California; current status and outlook as earthscope (PBO) is deployed, Open-File Rep, U.S. Geol. Surv. Roeloffs, E.A., Effects of fluid pressure changes on borehole strainmeter data; studies in preparation for the earthscope plate boundary observatory, Open-File Rep, U.S. Geol. Surv. Analyzing slip events along the Cascadia margin 1275 Roeloffs, E., Tidal calibration of plate boundary observatory borehole strainmeters: roles of vertical and shear coupling, J. geophys. 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Lett., 35, L15303, doi: /2008gl Wech, A.G., Creager, K.C. & Melbourne, T.I., Seismic and geodetic constraints on Cascadia slow slip, J. geophys. Res., 114, B10316, doi: /2008jb Zumberge, J.F., Heflin, M.B., Jefferson, D.C., Watkins, M.M. & Webb, F.H., Precise point positioning for the efficient and robust analysis of GPS data from large networks, J. geophys. Res., 102(B3), APPENDIX A For the second case in the comparison between co-located GPS and BSM station we choose baseline JORT-BAMF and not CART- BAMF due to the fact that the data from CART station contain an abrupt jump (both for kinematic and static positioning) which is not understood at this time, and might be due to a local effect (Fig. A2). Furthermore, we have also made sure that this abrupt jump was not due to any satellite or receiver clock errors, or changes in the GPS antenna phase centres. In addition, although BSM B003 is colocated with GPS station P403, we did not include it in our results due to the fact that the BSM areal strain is badly out of phase with the theoretical earth tides (which may be explained by the effects of pore fluid pressure), and also suffers from self-inconsistency (which means that different combinations of strain gauges from the same borehole produce considerably different values). These findings on BSM B003 are consistent with the results found by Roeloffs (2010).

8 1276 Y. Reuveni et al. Figure A1. Distribution of BSM sites (white circles with red outline) and GPS sites (black squares) that were used to calculate the baselines (brown lines) strain during the 2008 May slow slip event. A, B, C, D, E, F, G, H and I corresponds to the baselines BAMF-NTKA, PTRF-BAMF, JORT-PTRF, P403-JORT, SC03-P403, SC03-P418, P418-P430, P430-P417 and P417-P409, respectively.

9 Analyzing slip events along the Cascadia margin 1277 Figure A2. Four-hour position time-series for the GPS stations (blue lines) which were used in this study for calculating the different baselines, along with their daily position solutions (red dots). The abrupt jump (black rectangle) captured at the CART site, both for the east and north components, occurred during day 141 and lasted for 44 min.

10 1278 Y. Reuveni et al. Figure A2. (Continued.) Figure A3. Distance-time function used to model the bidirectional propagation of the 2008 slow slip event (red lines). Distance is measured from 45.8 latitude. The slip starts on day 125 of 2008 in the area of GPS site SC03 and propagates bidirectionally. The northwest propagation is initially at 8 km d 1, slows down to 2 km d 1 at day 140, and speeds up to 15 km d 1 at day 148. The south propagation is modelled using a uniform rate of 6 km d 1. Taken from Dragert & Wang (2011).