Surface wave tomography is a well established research paradigm at both global (e.g., Ekstrom et al., 1998; Ritzwoller et al., 1998; Trampert and Woodhouse, 2003) and regional scales (e.g., Ritzwoller et al., 1998). It is based on the measurement of the frequency dependence of Rayleigh and Love waves and their interpretation in terms of the geographical distribution of the speed of these waves. Traditionally, the method has been based on seismic energy emanating from earthquakes, which has allowed the development of tomographic maps such as that shown in Figure 1 for China. Maps such as this one, which represents the distribution of Rayleigh wave speeds at 16 sec across China, now exist world-wide and are the basis for inferences of the seismic wave speeds in the earth's crust and uppermost mantle globally (e.g., Shapiro and Ritzwoller, 2002) and regionally (e.g., Villasenor et al., 2001). The primary attraction of surface wave tomography is that the information learned tends to be distributed homogeneously over large areas and produces constraints on shear wave speeds in the earth's interior that complement other methods of seismic tomography based on compressional waves. The principal frustration of this research paradigm results from the inhomogeneous distribution of earthquakes. The net effect is to limit the spatial resolution or definition of the maps and also to restrict the frequency range or band-width of the maps. These effects, together, mean that traditional methods of surface wave tomography and inversion recover information only about very large-scale structures within the earth's interior and reveal very little information about the earth's crust. A new method of surface wave tomography has been developed very recently that breaks through these limitations (e.g., Shapiro et al., 2005). This method utilizes ``ambient seismic noise'' which is caused by atmospheric disturbances and ocean waves around the globe and pervades the earth at all times. Ambient noise tomography (ANT) is able to produce information about seismic wave propagation in much higher definition and at higher frequencies than the traditional methods of surface wave tomography based on earthquake waves. This is particularly true in regions of high seismic station density. ANT is now being applied in various regions around the world where station coverage is high (e.g., Tibet: Yao et al., 2006; Europe: Yang et al., 2007; North America: Mochetti et al., 2007; New Zealand: Lin et al, 2007; South Korea: Cho et al., 2006). An example of the distribution of Rayleigh wave speeds observed by ANT is shown in Figure 2 for the eastern US. This region, which has some similarities to the crustal structure of northeastern China, is relatively poorly instrumented. The stations within the US are primarily from the US backbone network (ANSS -- Advanced National Seismic System). Much higher station density is available in the western US (Figure 3) where the Transportable Array component of the EarthScope/USArray is now being deployed. Although tectonically distinct from northeastern China, the station distribution here is quite similar to the proposed distribution that will result from ChinArray and the maps of Figure 3 indicate the type of definition that is likely to result from the proposed research. Resolution at the inter-station spacing is achievable. Similar results to that shown in Figures 2 and 3 are also achieved for Love waves and phase speed can also be measured. Surface wave dispersion maps, such as those in Figures 2 and 3, are the basis for inversion to infer the 3D structure in the crust and uppermost mantle. An example of one aspect of a resulting 3D model obtained for California is shown in Figure 4. Information contained in models of isotropic wave speeds, of which the Moho depth map in Figure 4 is one aspect, is primarily static in nature, being about temperature and composition and, to a lesser extent, volatile (e.g., water) content. Observations of seismic anisotropy (both radial and azimuthal) provide information about the state of stress within the earth. Such dynamical information complements the static models. In particular, ANT provides a rich source of information about anisotropy of the earth's crust, which is difficult to achieve by other means (e.g., Shapiro et al., 2004). Figure 1. Rayleigh wave group speed across China at 20 sec period determined using traditional tomographic methods based on earthquakes. Results are presented as a percent perturbation to the average across the map. Figure 2. Rayleigh wave wave group speed map at 16 sec period presented as the percent deviation from the mean wave speed across the eastern US (3.1 km/sec). Station locations are shown with red triangles. The 75 km resolution contour is displayed and regions with very poor resolution are masked. Low wave speeds are associated with sedimentary basins. Most notably, the very low wave speeds in the Mississippi Gulf region result from thick accumulations of sediments. The generally higher wave speeds in Canada result from older crystalline rocks than in the US and thinner sediments. Figure 3. Rayleigh wave group speed maps in the western US resulting from EarthScope/USArray Transportable Array (TA) data acquired over 27 months (Oct. 2004 - Dec. 2006), presented in absolute velocity units (km/sec). The TA stations are shown at right with white triangles along with the inter-station paths used in tomography and the 75 km resolution contour. Features inside this contour are considered robust and worthy of interpretation. Maps at different periods (indicated) display different structures because the waves at these periods have different depth sensitivities. Examples of imaged features include the roots of mountains ranges such as the Sierra Nevada in eastern California, sedimentary basins such as the San Joaquin Valley, large igneous provinces such as the Snake River Plain in souther Idaho, volcanic provinces such as Yellowstone, and many other features. Figure 4. Example results of one aspect of a 3D model inferred from Ambient Noise Tomography: crustal thickness in Southern California. At left are crustal thickness estimates from painstakingly compiled receiver functions (Zhu and Kanamori, 2000; Yan and Clayton, 2006) and at right is crustal thickness from the inversion of ambient noise data. The results of these two disparate data sets are compatible, indicating that ambient noise tomography promises to produce high definition information about the crust over large regions. 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