4.6 Article

6-C polarization analysis using point measurements of translational and rotational ground-motion: theory and applications

Journal

GEOPHYSICAL JOURNAL INTERNATIONAL
Volume 213, Issue 1, Pages 77-97

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/gji/ggx542

Keywords

Time series analysis; Body waves; Rotational seismology; Surface waves and free oscillations

Funding

  1. CARNEVAL industry consortium (Nagra, OMV, Schlumberger Gould Research)
  2. Swiss National Supercomputing Centre (CSCS) [s703]
  3. Swiss National Science Foundation [200021_156996]
  4. Swiss National Science Foundation (SNF) [200021_156996] Funding Source: Swiss National Science Foundation (SNF)

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We provide a six-component (6-C) polarization model for P-, SV-, SH-, Rayleigh-, and Love-waves both inside an elastic medium as well as at the free surface. It is shown that single-station 6-C data comprised of three components of rotational motion and three components of translational motion provide the opportunity to unambiguously identify the wave type, propagation direction, and local P- and S-wave velocities at the receiver location by use of polarization analysis. To extract such information by conventional processing of three-component (3-C) translational data would require large and dense receiver arrays. The additional rotational components allow the extension of the rank of the coherency matrix used for polarization analysis. This enables us to accurately determine the wave type and wave parameters (propagation direction and velocity) of seismic phases, even if more than one wave is present in the analysis time window. This is not possible with standard, pure-translational 3-C recordings. In order to identify modes of vibration and to extract the accompanying wave parameters, we adapt the multiple signal classification algorithm (MUSIC). Due to the strong nonlinearity of the MUSIC estimator function, it can be used to detect the presence of specific wave types within the analysis time window at very high resolution. We show how the extracted wavefield properties can be used, in a fully automated way, to separate the wavefield into its different wave modes using only a single 6-C recording station. As an example, we apply the method to remove surface wave energy while preserving the underlying reflection signal and to suppress energy originating from undesired directions, such as side-scattered waves.

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