Noise reduction for broad-band, three-component seismograms using
data-adaptive polarization filters
Geophysical Journal International, 141 (3):
820--828 (June 2000)DOI: 10.1046/j.1365-246x.2000.00156.x
Abstract
We develop a data-adaptive polarization filter that can spectacularly
reduce microseismic noise contamination in three-component broad-band
seismograms. The filter uses a multitaper spectral analysis method
for computing the data spectral density matrix, which is defined
as an ensemble average of outer products of the spectrum and its
Hermitian adjoint. Under the assumption that strong noise in three-component,
broad-band seismograms is additive white noise, and that its spectral
density can be determined from seismogram segments without signals,
that is, a pre-signal arrival time window, we construct a data-adaptive
filter from a spectral density matrix that has been decontaminated
of noise. Since the noise corrupting the seismograms is complicated
and stochastic, the resulting residual due to the real, non-stationary
nature of microseismic noise can leave small-amplitude, quasi-sinusoidal,
background oscillations after filtering. These oscillations can be
removed by subsequent application of an optimum Wiener filter. Application
of the filter to synthetic data with real noise superimposed suppresses
the noise by about three orders of magnitude at the expense of less
than 5 per cent corruption of the original seismogram in amplitude.
Application to several real recordings of teleseismic earthquakes
on a three-component broad-band seismic station in Iceland shows
that excellent signal-to-noise recovery is possible, rendering such
data usable for both arrival time and waveform analysis. This technique
may potentially increase by an order of magnitude the volume of usable
data collected in seismic experiments in noisy environments, for
example, on oceanic islands.