Processing Seismic Ambient Noise Data with the Continuous Wavelet Transform to Obtain Reliable Empirical Green’s Function

Session: Advances in Seismic Interferometry: Theory, Computation and Applications
Type: Oral
Date: 4/30/2020
Time: 11:00 AM
Room: 230 + 235
Description: 

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) removal of 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 broadband 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. The new EGFs are next used as the input data to do the Full Waveform Tomography to construct a 3D velocity model for this area.

Presenting Author: Yang Yang

Authors