Yang Yang, Chunyu Liu, Charles A. Langston

We propose a new data processing flow to compute empirical Green’s functions (EGF) from ambient seismic noise based on a soft thresholding designaling and denoising method using the continuous wavelet transform. The designaling algorithm is carried out during the initial data processing to remove earthquakes and other transient signals in the seismic record. A continuous wavelet transform denoising algorithm removes the noise in the final stacked cross-correlogram. The overall data processing procedure is divided into four stages: (1) single station data preparation, (2) remove earthquakes and other signals in the seismic record, (3) spectrum whitening, cross-correlation and temporal stacking, (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 datasets. Synthetic data constructed with a recorded earthquake and recorded ambient noise is used to test the designaling method. We then apply the new processing flow to data recorded by the USArray Transportable Array stations near the New Madrid Seismic Zone where many seismic events and transient signals are observed in the data. We compare the EGFs calculated from our new flow with time domain normalization and our results show improved signal-to-noise ratios and deliver more reliable measurements that can be used for further processing. The designaling method improves the homogeneity of the ambient noise wavefield which is an intrinsic requirement for seismic interferometry. The final denoising step suppresses random noise and provides clearer EGFs for the next processing step.