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SUMMARY Estimation of ambient seismic source distributions (e.g. location and strength) can aid studies of seismic source mechanisms and subsurface structure investigations. One can invert for the ambient seismic (noise) source distribution by applying full-waveform inversion (FWI) theory to seismic (noise) crosscorrelations. This estimation method is especially applicable for seismic recordings without obvious body-wave arrivals. Data pre-processing procedures are needed before the inversion, but some pre-processing procedures commonly used in ambient noise tomography can bias the ambient (noise) source distribution estimation and should not be used in FWI. Taking this into account, we propose a complete workflow from the raw seismic noise recording through pre-processing procedures to the inversion. We present the workflow with a field data example in Hartoušov, Czech Republic, where the seismic sources are CO2 degassing areas at Earth’s surface (i.e. a fumarole or mofette). We discuss factors in the processing and inversion that can bias the estimations, such as inaccurate velocity model, anelasticity and array sensitivity. The proposed workflow can work for multicomponent data across different scales of field data.

Estimates of S-wave velocity with depth from Rayleighwave dispersion data are limited by the accuracy of fundamental and/or higher mode signal identification. In many scenarios, the fundamental mode propagates in retrograde motion, whereas higher modes propagate in prograde motion. This difference in particle motion (or polarity) can be used by joint analysis of vertical and horizontal inline recordings. We have developed a novel method that isolates modes by separating prograde and retrograde motions; we call this a polarity mute. Applying this polarity mute prior to traditional multichannel analysis of surface wave (MASW) analysis improves phase velocity estimation for fundamental and higher mode dispersion. This approach, in turn, should lead to improvement of S-wave velocity estimates with depth. With two simple models and a field example, we have highlighted the complexity of the Rayleigh-wave particle motions and determined improved MASW dispersion images using the polarity mute. Our results show that we can separate prograde and retrograde signals to independently process fundamental and higher mode signals, in turn allowing us to identify lower frequency dispersion when compared with single component data. These examples demonstrate that the polarity mute approach can improve estimates of S-wave velocities with depth.

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