Template-matching-based data selection for passive seismic surface wave tomography

Ambient noise tomography (ANT) has been widely used to determine near surface shear-wave velocity (V_S) model. To fulfill the randomization requirement of the stationary source distribution for ANT, temporal averaging over a sufficiently long period of time is needed. However, in small-scale passive surface wave tomography, it is difficult to realize long-time observation and as a result non-stationary sources could affect the quality of the stacked dispersion measurements. In this study, we proposed a template-matching-based data selection method to obtain high-quality cross-correlation functions for dispersion analysis by only selecting time segments that are associated with similar cross-correlation functions with the template. One simple way to create the template is by stacking cross-correlations for all time segments. Two synthetic tests have demonstrated the strength of the proposed technique on recovering accurate dispersion curves. Field data analysis further proves the applicability of the proposed technique in selecting high-quality data segments with bin-stacked template. These tests show that the proposed method offers an efficient data processing method for passive surface-wave tomography.