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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We present a high-resolution, three-dimensional P-wave seismic velocity model of the sedimentary basin in the Salton Trough, southern California, and use the model for spectral-element method (SEM) wave propagation and groundmotion simulations to quantitatively assess seismic hazard in the region. The basin geometry is defined by a surface representing the top of crystalline basement, which was constrained by seismic refraction profiles and free-air gravity data. Sonic logs from petroleum wells in the Imperial Valley and isovelocity surfaces defined by seismic refraction studies were used to define P-wave velocity within the sedimentary basin as a function of two variables:(1) absolute depth and (2) depth of the underlying crystalline basement surface (CBS). This velocity function was used to populate cells of a three-dimensional spatial array (voxet) defining the P-wave velocity structure in the basin. The new model was then resampled in a computational mesh used for earthquake wave propagation and strong ground motion simulations based upon the SEM . Simulation of the 3 November 2002 M w 4.2 Yorba Linda earthquake demonstrates that the new model provides accurate simulation of strong ground motion amplification effects in the Salton Trough sedimentary basin, offering substantial improvements over previous models. A hypothetical M w 7.9 earthquake on the southern San Andreas fault was then simulated in an effort to better understand the seismic hazard associated with the basin structure. These simulations indicate that great amplification will occur during large earthquakes in the region due to the low seismic velocity of the sediments and the basin shape and depth.

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