The GTL implemented in the USR was based on Ely et al. (2010) and uses the geology-based Vs30 maps of Wills and Clahan (2006) to specify velocity values at the Earths surface in the voxet. V P , and in turn density, are inferred from surface V S using the scaling laws of Brocher (2005). These values were parameterized to a depth of z T = 350 meters with the following formulations:where z ′ is depth, V ST and V P T are are S-and P-wave velocities extracted from the crustal velocity model at depth z T , P () is the Brocher (2005) P-wave velocity scaling law, andThe coefficient a controls the ratio of surface velocity to original 30 meter average, b controls overall curvature, and c controls near-surface curvature of the velocity profile. The coefficients a = 1/2, b = 2/3, and c = 3/2 were chosen to fit the generic rock profile of Boore and Joyner (1997) while also producing smooth and well-behaved profiles when combined with the underlying basin and crustal velocity models (Ely et al., 2010) ( Figure 7). S2 Model validation, comparison, and uncertaintyThe velocity model (CVM) component of the USR described here is assembled from several different data sets and models, and thus it is challenging to formally assess model resolution and uncertainties. One clear step for the sedimentary basins is to assess the variability in well data that is not represented in the final model. As we discussed, these data measure interval transit times over borehole distances of less than 1 m, whereas the velocity model uses smoothed (25 m sampled) versions of these data. To make this assessment, we compared observations directly with the velocity values represented at 108 well bore locations in the Los Angeles basin. Our analysis shows a standard deviation of 6.5% around a mean of 1.0 for the ratio between compressional wave slowness in logs and the model in a population of ca. 1.1 million samples. This corresponds to a standard deviation in V P of ±99 m/s at 2000 m/s.

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S U M M A R YThis paper presents a verification of three simulations of the ShakeOut scenario, an M w 7.8 earthquake on a portion of the San Andreas fault in southern California, conducted by three different groups at the Southern California Earthquake Center using the SCEC Community Velocity Model for this region. We conducted two simulations using the finite difference method, and one by the finite element method, and performed qualitative and quantitative comparisons between the corresponding results. The results are in good agreement with each other; only small differences occur both in amplitude and phase between the various synthetics at ten observation points located near and away from the fault-as far as 150 km away from the fault. Using an available goodness-of-fit criterion all the comparisons scored above 8, with most above 9.2. This score would be regarded as excellent if the measurements were between recorded and synthetic seismograms. We also report results of comparisons based on time-frequency misfit criteria. Results from these two criteria can be used for calibrating the two methods for comparing seismograms. In those cases in which noticeable discrepancies occurred between the seismograms generated by the three groups, we found that they were the product of inherent characteristics of the various numerical methods used and their implementations. In particular, we found that the major source of discrepancy lies in the difference between mesh and grid representations of the same material model. Overall, however, even the largest differences in the synthetic seismograms are small. Thus, given the complexity of the simulations used in this verification, it appears that the three schemes are consistent, reliable and sufficiently accurate and robust for use in future large-scale simulations.