LAUSR

Obtaining reliable empirical Green's functions (EGFs) from ambient noise by seismic interferometry requires homogeneously distributed noise sources. However, it is difficult to attain this condition since ambient noise data usually contain highly correlated signals from earthquakes or other transient sources from human activities. Removing these transient signals is one of the most essential steps in the whole data processing flow to obtain EGFs. We propose to use a denoising method based on the continuous wavelet transform to achieve this goal. The noise level is estimated in the wavelet domain for each scale by determining the 99 per cent confidence level of the empirical probability density function of the noise wavelet coefficients. The correlated signals are then removed by an efficient soft thresholding method. The same denoising algorithm is also applied to remove the noise in the final stacked cross-correlogram. A complete data processing workflow is provided with the overall data processing procedure divided into four stages: (1) single station data preparation, (2) removal of earthquakes and other transient signals in the seismic record, (3) spectrum whitening, cross-correlation and temporal stacking and (4) remove the noise in the stacked cross-correlogram to deliver the final EGF. The whole process is automated to make it accessible for large data sets. Synthetic data constructed with a recorded earthquake and recorded ambient noise is used to test the denoising method. We then apply the new processing workflow to data recorded by the USArray Transportable Array stations near the New Madrid Seismic Zone where many seismic events and transient signals are observed. We compare the EGFs calculated from our workflow with commonly used time domain normalization method and our results show improved signal-to-noise ratios. The new workflow can deliver reliable EGFs for further studies.