Toward Understanding Crustal Body Wave Recovery with Ambient Noise Seismic Interferometry Applied to USArray

Thursday, 18 December 2014
Celeste Ritter Labedz1, Dylan Mikesell2, Piero Poli2 and German A Prieto2, (1)University of Nebraska Lincoln, Lincoln, NE, United States, (2)Massachusetts Institute of Technology, Cambridge, MA, United States
Cross-correlation of the ambient seismic field is now widely applied for imaging and monitoring at many scales. This method has been quite successful in retrieving surface wave information, which can be used for estimating three-dimensional shear velocity structure, and in some cases estimating anisotropy or wave amplification and attenuation. However, the use of this approach to retrieve crustal body waves has seen less widespread use. While some studies (e.g., Zhan et al. 2010, Poli et al. 2012) have successfully recovered phases over a few hundred kilometers on continental shields, crustal body waves are not yet seen routinely over longer distances and in more structurally complex regions. In this study, we investigate the recovery of crustal body waves in the continental USA using stacked cross-correlations. The data for correlation was gathered over three to five years of continuous recording on an east-to-west line of USArray stations spanning the northern USA. Specifically, we study four parameters to determine which combination of processing produces the most robust crustal body wave estimates in this geologic setting: 1) the role of the total amount of data; 2) the influence of the length of the correlation time windows; 3) the effect of the geographic region of data collection; 4) the impact of different processes for selecting which noise windows go into the final stacks. In the last, we consider two methods to discriminate “good” and “bad” noise correlations: comparison of the amplitude of each correlation trace and matching the correlation window times with a global earthquake catalog. We are able to recover short period crustal S-wave phases at as far as 1300 kilometer interstation distances, which will provide unique information for future tomography models.