Improving Surface Wave Retrieval From Traffic Noise by Deconvolution of the Decomposed Wavefield

Abstract

Traffic noise is an important type of passive seismic data because it usually includes strong dispersive surface wave components and can be easily accessed. It can be used to extract virtual surface waves via seismic interferometry algorithms for the purpose of imaging subsurface shear wave velocity distribution. In this paper, we propose a scheme to improve the retrieval of surface waves from traffic noise recorded using linear arrays along traffic roads. By deconvolving the decomposed traffic noise wavefield, robust surface wave traces can be computed from a short noise record. First the far-field component of the traffic noise recording is extracted and separated into unidirectionally propagating components. Then deconvolution interferometry is applied to these separated far-field wavefield to extract surface wave Green's function. With this scheme, crosstalk noise and near-field artifacts are excluded from the computation, and surface wave traces with high signal-to-noise ratio (SNR) are achieved using short traffic noise traces. In a synthetic test virtual surface waves estimated with the proposed method show significantly higher SNR than those computed with the conventional interferometry workflows, and matches well with simulated active source traces. A field data example with traffic noise recorded in a distributed acoustic sensing experiment also shows that surface waves estimated using the proposed methodology demonstrate higher SNR than those computed with the conventional interferometry schemes and that the virtual surface waves generated using 4 s of traffic noise demonstrate signal quality comparable to the surface waves recorded in this experiment with an active source.