ABSTRACTIn regions where active source seismic exploration is constrained by limitations of energy penetration and recovery, cost and logistical concerns, or regulatory restrictions, analysis of natural source seismic data may provide an alternative. In this study, we investigate the feasibility of using locally‐generated seismic noise in the 2–6 Hz band to obtain a subsurface model via interferometric analysis. We apply this technique to three‐component data recorded during the La Barge Passive Seismic Experiment, a local deployment in south‐western Wyoming that recorded continuous seismic data between November 2008 and June 2009. We find traffic noise from a nearby state road to be the dominant source of surface waves recorded on the array and observe surface wave arrivals associated with this source up to distances of 5 kms. The orientation of the road with respect to the deployment ensures a large number of stationary points, leading to clear observations on both in‐line and cross‐line virtual source‐receiver pairs. This results in a large number of usable interferograms, which in turn enables the application of standard active source processing methods like signal processing, common offset stacking and traveltime inversion. We investigate the dependency of the interferograms on the amount of data, on a range of processing parameters and on the choice of the interferometry algorithm. The obtained interferograms exhibit a high signal‐to‐noise ratio on all three components. Rotation of the horizontal components to the radial/transverse direction facilitates the separation of Rayleigh and Love waves. Though the narrow frequency spectrum of the surface waves prevents the inversion for depth‐dependent shear‐wave velocities, we are able to map the arrival times of the surface waves to laterally varying group and phase velocities for both Rayleigh and Love waves. Our results correlate well with the known geological structure. We outline a scheme for obtaining localized surface wave velocities from local noise sources and show how the processing of passive data benefits from a combination with well‐established exploration seismology methods. We highlight the differences with interferometry applied to crustal scale data and conclude with recommendations for similar deployments.
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