International audienceAmbient noise tomography is a rapidly emerging field of seismological research. This paper presents the current status of ambient noise data processing as it has developed over the past several years and is intended to explain and justify this development through salient examples. The ambient noise data processing procedure divides into four principal phases: (1) single station data preparation, (2) cross-correlation and temporal stacking, (3) measurement of dispersion curves (performed with frequency–time analysis for both group and phase speeds) and (4) quality control, including error analysis and selection of the acceptable measurements. The procedures that are described herein have been designed not only to deliver reliable measurements , but to be flexible, applicable to a wide variety of observational settings, as well as being fully automated. For an automated data processing procedure, data quality control measures are particularly important to identify and reject bad measurements and compute quality assurance statistics for the accepted measurements. The principal metric on which to base a judgment of quality is stability, the robustness of the measurement to perturbations in the conditions under which it is obtained. Temporal repeatability, in particular, is a significant indicator of reliability and is elevated to a high position in our assessment, as we equate seasonal repeata-bility with measurement uncertainty. Proxy curves relating observed signal-to-noise ratios to average measurement uncertainties show promise to provide useful expected measurement error estimates in the absence of the long time-series needed for temporal subsetting

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S U M M A R YWe present the results of Rayleigh wave and Love wave phase velocity tomography in the western United States using ambient seismic noise observed at over 250 broad-band stations from the EarthScope/USArray Transportable Array and regional networks. All available threecomponent time-series for the 12-month span between 2005 November 1 and 2006 October 31 have been cross-correlated to yield estimated empirical Rayleigh and Love wave Green's functions. The Love wave signals were observed with higher average signal-to-noise ratio (SNR) than Rayleigh wave signals and hence cannot be fully explained by the scattering of Rayleigh waves. Phase velocity dispersion curves for both Rayleigh and Love waves between 5 and 40 speriod were measured for each interstation path by applying frequency-time analysis. The average uncertainty and systematic bias of the measurements are estimated using a method based on analysing thousands of nearly linearly aligned station-triplets. We find that empirical Green's functions can be estimated accurately from the negative time derivative of the symmetric component ambient noise cross-correlation without explicit knowledge of the source distribution. The average traveltime uncertainty is less than 1 s at periods shorter than 24 s. We present Rayleigh and Love wave phase speed maps at periods of 8, 12, 16,and 20 s. The maps show clear correlations with major geological structures and qualitative agreement with previous results based on Rayleigh wave group speeds.

Summary We describe a method to invert surface wave dispersion data for a model of shear velocities with uncertainties in the crust and uppermost mantle. The inversion is a multistep process, constrained by a priori information, that culminates in a Markov‐chain Monte‐Carlo sampling of model space to yield an ensemble of acceptable models at each spatial node. The model is radially anisotropic in the uppermost mantle to an average depth of about 200 km and is isotropic elsewhere. The method is applied on a 2°× 2° grid globally to a large data set of fundamental mode surface wave group and phase velocities (Rayleigh group velocity, 16–200 s; Love group velocity, 16–150 s; Rayleigh and Love phase velocity, 40–150 s). The middle of the ensemble (Median Model) defines the estimated model and the half‐width of the corridor of models provides the uncertainty estimate. Uncertainty estimates allow the identification of the robust features of the model which, typically, persist only to depths of ∼250 km. We refer to the features that appear in every member of the ensemble of acceptable models as ‘persistent’. Persistent features include sharper images of the variation of oceanic lithosphere and asthenosphere with age, continental roots, extensional tectonic features in the upper mantle, the shallow parts of subducted lithosphere, and improved resolution of radial anisotropy. In particular, we find no compelling evidence for ‘negative anisotropy’ anywhere in the world's lithosphere.