Zusammenfassung
The design of data-adaptive filters requires that the noise be defined,
statistically or otherwise, by parameters which allow some means
of separating the noise from the signal. We consider here multichannel
data in which one knows only that the noise is less polarized than
the signal in a unitary space. This description of the noise is not
sufficient for designing filters which are optimum in any sense;
consequently, the filters may require a number of changes in the
parameters before a satisfactory design can be found. Once this design
has been achieved, the filters can be used to enhance waveforms of
arbitrary shape, requiring little prior knowledge of the spectral
content or temporal features of the signal. In contrast to many other
data-adaptive filters which give a scalar time-series output, the
filters we describe here with vector time series input have an equal
number of input and output channels. A number of examples of filtered
magnetic and seismic data are given in order to emphasize the wide
range of uses for the filters. Some suggestions for application of
the filters to multichannel seismic data are given.
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