Processing Ambient Noise Data Using Phase Cross-Correlation and Application Toward Understanding Spatiotemporal Environmental Effects

Typical use of ambient noise interferometry focuses on longer period (>1 s) waves for exploration of subsurface structure and other applications, while very shallow structure and some environmental seismology applications may benefit from use of shorter period (<1 s) waves. We explore the potential for short-period ambient noise interferometry to determine shallow seismic velocity structures by comparing two methodologies, the conventional amplitude-based cross-correlation and linear stacking (TCC-Lin) and a more recently developed phase cross-correlation and time-frequency phase-weighted-stacking (PCC-PWS) method with both synthetic and real data collected in a heterogeneous karst aquifer system. Our results suggest that the PCC-PWS method is more effective in extracting short-period wave velocities than the TCC-Lin method, especially when using data collected in regions containing complex shallow structures such as the karst aquifer system investigated here. In addition to the different methodologies for computing the cross correlation functions, we also examine the relative importance of signal-to-noise ratio and number of wavelengths propagating between station pairs to determine data/solution quality. We find that the lower number of wavelengths of 3 has the greatest impact on the network-averaged group velocity curve. Lastly, we test the sensitivity of the number of stacks used to create the final empirical Green's function, and find that the PCC-PWS method required about half the number of cross-correlation functions to develop reliable velocity curves compared to the TCC-Lin method. This is an important advantage of the PCC-PWS method when available data collection time is limited.

Automated summary

The phase cross-correlation and time-frequency phase-weighted-stacking (PCC-PWS) method is more effective than the conventional amplitude-based cross-correlation and linear stacking (TCC-Lin) method in extracting short-period wave velocities, especially in regions with complex shallow structures. The signal-to-noise ratio and the number of wavelengths propagating between station pairs have a significant impact on the data/solution quality. The PCC-PWS method requires fewer cross-correlation functions compared to the TCC-Lin method, making it advantageous in limited data collection time.