Combined application of FTAN and cross-spectra analysis to ambient noise recorded by a microseismic monitoring network

A case study of seismic interferometry applied to a small microseismic monitoring network is here presented. The main objectives of this study are (i) to quantify the lateral variability of shear-wave velocities in the studied area, and (ii) to investigate the bias produced by noise directionality and non-stationarity in the velocity estimate. Despite the limited number of stations and the short-period character of the seismic sensors, the empirical Green's functions were retrieved for all station pairs using two years of passive data. Both group and phase velocities were derived, the former using the widespread frequency-time analysis, the latter through the analysis of the real part of the cross-spectra. The main advantage of combining these two methods is a more accurate identification of higher modes, resulting in a reduction of ambiguity during picking and data interpretation. Surface wave tomography was run to obtain the spatial distribution of group and phase velocities for the same wavelengths. The low standard deviation of the results suggests that the sparse character of the network does not limit the applicability of the method, for this specific case. The obtained maps highlight the presence of a lower velocity area that extends from the centre of the network towards southeast. Group and phase velocity dispersion curves have been jointly inverted to retrieve as many shear-wave velocity profiles as selected station pairs. While the average model can be used for a more accurate location of the local natural seismicity, the associated standard deviations give us an indication of the lateral heterogeneity of seismic velocities as a function of depth. Finally, the same velocity analysis was repeated for different time windows in order to quantify the error associated to variations in the noise field. Errors as large as 4% have been found, related to the unfavorable orientation of the receiver pairs with respect to strongly directional noise sources, and to the very short time widows. It was shown that using a one-year time window these errors are reduced to 0.3%. • Seismic velocities derived from ambient noise recorded by a sparse seismic network. • Combining two different surface wave methods ensures a better data interpretation. • Velocity spatial variability and noise source non-stationarity investigated.