We present a singular value decomposition (SVD) based algorithm for polariz
ation filtering of triaxial seismic recordings based on the assumption that
the particle motion trajectory is essentially 2-D (elliptical polarization
).
The filter is the sum of the first two eigenimages of the SVD on the signal
matrix. Weighing functions, which are strictly dependent on the intensity
(linearity and planarity) of the polarization, are applied. The efficiency
of the filter is tested on synthetic traces and on real data, and found to
be superior to solely covariance-based filter algorithms. Although SVD and
covariance-based methods have similar theoretical approach to the solution
of the eigenvalue problem, SVD does not require any further rotation along
the polarization ellipsoid principal axes. The algorithm presented here is
a robust and fast filter that properly reproduces polarization attributes,
amplitude, and phase of the original signal. A major novelty is the enhance
ment of both elliptical and linear polarized signals. Moreover as SVD prese
rves the amplitude ratios across the triaxial recordings, the particle moti
on ellipse before and after filtering retains a correct orientation, overco
ming a typical artifact of the covariance-based methods.