Eikonal Tomography Using Coherent Surface Waves Extracted From Ambient Noise by Iterative Matched Filtering—Application to the Large‐N Maupasacq Array

Abstract

AbstractStandard ambient noise tomography relies on cross‐correlation of noise records between pairs of sensors to estimate empirical Green's functions. This approach is challenging if the distribution of noise sources is heterogeneous and can get computationally intensive for large‐N seismic arrays. Here, we propose an iterative matched filtering method to isolate and extract coherent wave fronts that travel across a dense array of seismic sensors. The method can separate interfering wave trains coming from different directions, to provide amplitude and travel time fields for each detected wave front. We use the eikonal equation to derive phase velocity maps from the gradient of these travel time fields. Artifacts originating from scattered waves are removed by azimuthal averaging and spatial smoothing. The method is validated on a synthetic test and then applied to the data of the Maupasacq experiment. Rayleigh wave phase velocity maps are obtained for periods between 2 and 9 s. These maps correlate with surface geology at short period (T<3 s) and reveal the deep architecture of the Arzacq and Mauleon basins at longer periods (T>4 s).