S34B-04
A Spatial Coherence Analysis of Seismic Wavefields Based on Array Covariance Matrix : Application to One Year of the USArray Data

Wednesday, 16 December 2015: 16:45
307 (Moscone South)
Leonard Seydoux1, Nikolai Shapiro2, Julien de Rosny3 and Matthieu Landes2, (1)Institut de Physique du Globe de Paris, Paris Sorbonne Cité, CNRS, Paris, France, (2)Institut de Physique du Globe de Paris, Paris, France, (3)Instiut Langevin, ESPCI ParisTech, PSL Research University, CNRS, Paris, France
Abstract:
Theoretical demonstrations of the Green’s function reconstruction from cross-correlations of the ambient noise rely on a strong hypothesis about the spatial homogeneity of the noise sources distribution. However, real seismic noise within the Earth is not homogeneously distributed. In particular, strongly coherent signals from earthquakes or localized noise sources might be harmful to the application of this method. Most of signal pre-processing techniques used to compensate for the wavefield inhomogeneity are based on temporal or spectral normalization of individual seismograms.

Here we consider an approach based on simultaneous analysis of records from multiple sensors of a seismic array. We perform a time-frequency analysis of the eigenvalue spectrum of the covariance matrix. In a case of a wavefield dominated by a single noise source, the rank of the array covariance matrix is very low. Consequently, its eigenvalue spectrum contains a single dominant value. In a case of a well distribution of uncorrelated noise sources, the matrix rank increases and the eigenvalue spectrum is much broader. Therefore, we use the width of the eigenvalue spectrum as a measure of the level of the wavefield spatial coherence. The results are interpreted within the random matrix theory.

We apply this approach on the wavefield recorded during the year 2010 by the USArray, and evaluate the effect of the energy normalization usually applied to single data prior to cross-correlation computation. We show that many coherent events resist to this normalization, and are still harmful to ambient noise tomography. We finally propose a new way to improve the energy normalization of data. We modify the eigenvalue spectrum of the covariance matrix to mitigate the effect of strong events. We show that cross-correlation symmetry is improved with this new technique, that may allow to use noise cross-correlations in zones where the seismic activity usually prevents the use of ambient seismic noise in the analysis of the Earth’s crust.