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earthquake. Unfortunately clouds hampered acquisition until21 September 2010, 17 days after the earthquake. We extractedthe 17.9-km-swath-wide, half-meter panchromatic imageryusing identical procedures as with the GeoEye-1 imagery.Difficulties arise using this high-resolution imagery dueto agricultural changes in the intervening year and differentsun elevations and azimuths that result in a variable degree ofshadowing from houses and hedgerows. Much of the imagerydecorrelates over this time interval, in part because there havebeen dramatic changes in land use that are visible in the form ofradical differences in relative brightness between fields and differentplowing patterns between the two images. However, thehedgerows themselves, which are visually distorted across thefault in the postseismic images (Figures 2B–D), act as coherentfeatures that provide very strong offsets from image to image.Since the hedgerows are effectively linear and have a similarbrightness along their length, the offsets are better-resolved ina direction perpendicular to each hedgerow than they are alongtheir length. Therefore, we obtain good characterization of theE-W deflection of N-S-trending hedgerows across the fault, butpoor results for E-W motion of hedgerows and roads that trendin a near E-W direction. Since the horizontal displacements inthe E-W direction are much larger than those in the N-S directionfor this earthquake, the most useful features in the imagerypixel tracking have been the N-S-trending roads and hedgerows.Figure 3 summarizes the results of optical imagery pixeltracking for the Darfield earthquake. Colored dots (Figure 3A)indicate the magnitude of displacement in an E-W directionof a 10 × 10 pixel box that was allowed to move for 32 pixelsin any direction, posted at 5-pixel spacing. Peak displacementsacross the fault (Figure 3B) agree with what one would pick fromthe trend of the hedgerow using the postseismic imagery alone(Figure 2D). Although offsets in this example are only recoverablefrom anthropogenic features, processing images withshorter temporal baselines (days to months) produces coherentoffsets in vegetated regions, validating that this technique canbe used in remote regions if appropriate acquisitions are available.Unfortunately, the only imagery available with these shorttemporal baselines is located away from the Darfield fault trace.MODELING RESULTSSource ModelingDarfield EarthquakeFor the Darfield and Christchurch earthquakes, we invert thegeodetic observations for spatially distributed fault slip usingplanar fault geometries that we infer using a combination ofnonlinear inversion and independent data such as surface ruptures,aftershocks, etc. For the Darfield earthquake, we use foursteeply south-dipping planes (Table S1) to model the primarilyright-lateral strike-slip motion (Figures 4A and 4C) using a linearinversion for spatially distributed fault slip on a set of 328triangular dislocations (Meade 2007) with minimum momentregularization constraints. Beavan et al. (2010) demonstratedthat shallow (~4 km) thrust slip in addition to right-lateral slip isrequired to fully account for all features in the deformation field;however, our primary goal in interpreting the Darfield earthquakedeformation field is to drive modeling of Coulomb stresschange at the location of the Christchurch earthquake. At thesedistances, the effects of the shallow thrust faults are not likely tohave a strong effect on Coulomb stress change (e.g., King 2009).Our Darfield fault model location is based on mapped surfaceruptures (Quigley et al. 2010) while dips are constrained by focalmechanisms of right-lateral aftershocks. We extend our faults tothe east and west to account for significant deformation apparentin the interferograms beyond mapped surface ruptures. Ourbest-fit slip distribution and model residual is shown in Figure4A, with a moment magnitude of Mw 7.0. Slip magnitudes anddepth ranges agree well with previous inversions by Beavan et al.(2010) using InSAR and GPS observations. We are unable to fitsome features in the data near the center and easternmost end ofthe rupture (Figure S1). The misfit is influenced by a combinationof errors in model geometry, exclusion of NE-SW-dippingreverse faults, spatially correlated atmospheric noise, ionosphericperturbations, and contributions from significant postseismicdeformation evident in postseismic interferograms (Figure S3)and, therefore, likely present in varying degrees in the coseismicinterferograms used in our inversions.Figure 3B illustrates the predicted E-W horizontal offsetsfrom our best-fit model at the location of the optical imagepixel-tracking results. The predicted displacements across thefault are significantly smaller (~2.5 m compared with 5 m),which is not surprising given that there was a data gap in theInSAR imagery approaching the fault and that the regularizationplaced on our inversion tends to reduce slip in regions thathave less coverage by the data. The discrepancy may also be due,in part, to variable amounts of postseismic slip between theinterferograms and the optical imagery. Overall, the differencebetween the observed displacements and those predicted usinginversions based on InSAR data and an elastic half-space modelhighlights both the importance of using near-field data when itexists as well as the potential for issues in using elastic modelsin regions where the deformation is clearly anelastic. However,these issues are likely to primarily affect the inversion for slipin the shallow subsurface and will not contribute much to thepredicted Coulomb stress study discussed below.Christchurch EarthquakesTo obtain a fault model for the Christchurch earthquakes, weuse the Neighborhood Algorithm (Sambridge 1999) to invertALOS-PALSAR and Envisat interferograms (Table 1) for singlefault dislocations (Table S1). We then fix this best-fit geometryand extend the fault along-strike and down-dip to avoid spuriousedge effects before performing a linear inversion for distributedslip. Model trace locations are shown in Figure 4C. We use anautomated, resolution-based fault parameterization (Barnhartand Lohman 2010) that generates smaller fault patches near thesurface, where there is more constraint from data, than at depthand offshore, which allows us to more efficiently explore a rangeof potential fault geometries and constraints on slip than if weused a uniform fault patch size distribution. For the 22 FebruarySeismological Research Letters Volume 82, Number 6 November/December 2011 819