  We present a technique to automatically classify the wave type of seismic
phases that are recorded on a single six-component recording station (measuring
both three components of translational and rotational ground motion) at the
earth's surface. We make use of the fact that each wave type leaves a unique
'fingerprint' in the six-component motion of the sensor. This fingerprint can
be extracted by performing an eigenanalysis of the data covariance matrix,
similar to conventional three-component polarization analysis. To assign a wave
type to the fingerprint extracted from the data, we compare it to analytically
derived six-component polarization models that are valid for pure-state plane
wave arrivals. For efficient classification, we make use of the supervised
machine learning method of support vector machines that is trained using
data-independent, analytically-derived six-component polarization models. This
enables the rapid classification of seismic phases in a fully automated
fashion, even for large data volumes, such as encountered in land-seismic
exploration or ambient noise seismology. Once the wave-type is known,
additional wave parameters (velocity, directionality, and ellipticity) can be
directly extracted from the six-component polarization states without the need
to resort to expensive optimization algorithms.
  We illustrate the benefits of our approach on various real and synthetic data
examples for applications such as automated phase picking, aliased ground-roll
suppression in land-seismic exploration, and the rapid close-to real time
extraction of surface wave dispersion curves from single-station recordings of
ambient noise. Additionally, we argue that an initial step of wave type
classification is necessary in order to successfully apply the common technique
of extracting phase velocities from combined measurements of rotational and
translational motion.
