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

Ð We describe a method to invert regional or global scale surface-wave group or phasevelocity measurements to estimate 2-D models of the distribution and strength of isotropic and azimuthally anisotropic velocity variations. Such maps have at least two purposes in monitoring the nuclear Comprehensive Test-Ban Treaty (CTBT): (1) They can be used as data to estimate the shear velocity of the crust and uppermost mantle and topography on internal interfaces which are important in event location, and (2) they can be used to estimate surface-wave travel-time correction surfaces to be used in phasematched ®lters designed to extract low signal-to-noise surface-wave packets.The purpose of this paper is to describe one useful path through the large number of options available in an inversion of surface-wave data. Our method appears to provide robust and reliable dispersion maps on both global and regional scales. The technique we describe has a number of features that have motivated its development and commend its use: (1) It is developed in a spherical geometry; (2) the region of inference is de®ned by an arbitrary simple closed curve so that the method works equally well on local, regional, or global scales; (3) spatial smoothness and model amplitude constraints can be applied simultaneously; (4) the selection of model regularization and the smoothing parameters is highly¯exible which allows for the assessment of the eect of variations in these parameters; (5) the method allows for the simultaneous estimation of spatial resolution and amplitude bias of the images; and (6) the method optionally allows for the estimation of azimuthal anisotropy.We present examples of the application of this technique to observed surface-wave group and phase velocities globally and regionally across Eurasia and Antarctica.

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[1] We determine the effect of replacing geometrical ray theory in surface wave tomography with scattering theory. We describe a tomographic method based on a simplified version of the scattering sensitivity kernels that emerge from the Born or Rytov approximations in which surface wave travel times are a weighted average of phase or group slowness over the first Fresnel zone of the wave. We apply this ''diffraction tomography'' to Rayleigh and Love wave group velocity measurements to produce group velocity maps from 20 to 150 s period on a 2°Â 2°grid globally. Using identical data and damping parameters, we also produce maps using ''Gaussian tomography'' which is based on ray theory with intuitive Gaussian smoothing constraints. Significant differences in the amplitude and geometry of the imaged features appear primarily at long periods but exist even in the short-period maps in regions where average path lengths are large. Diffraction tomography, therefore, is significant in most oceanic regions at all periods, but it is also important on continents at long periods at least. On average, diffraction tomography produces larger velocity anomalies in a period-dependent band of spherical harmonic degrees, and diffraction and Gaussian tomography maps decorrelate past a critical spherical harmonic degree that also depends on period. The widths of resolving kernels that emerge from diffraction tomography are systematically larger than those from Gaussian tomography. Finally, mantle features inferred from diffraction tomography tend to have larger amplitudes and extend deeper than those from Gaussian tomography.

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