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kinds of deconvolution are generally described by the different
adjectives. They usually designate the type of assumptions made in the
can be used to remove the effects of the recording system, if the system
characteristics are known. This type also can be used to remove the
ringing that results from waves undergoing multiple bounces in the water
layer, if the travel time in the water layer and the reflectivity of the
seafloor are known.
case of the earth, the previous filtering that was applied is not known,
and thus the deconvolution takes on a statistical nature. In this
situation the needed information comes from an autocorrelation of the
seismic trace. Because the embedded wavelet from the source is repeated
at each reflecting interface, this repetition is captured by the
autocorrelation and used to design the inverse filter.
embedded wavelet ordinarily dominates the early part of an
autocorrelation, whereas multiples dominate the later part. Hence
different parts of the autocorrelation are used to determine different
filters for different types of deconvolution. The embedded wavelet then
can be recovered from the early part of the autocorrelation, but,
because the autocorrelation contains amplitude information only, an
assumption about phase is required. Minimum phase in the recorded data
is usually assumed, and normally this is a good assumption. The output
of the deconvolution, however, is normally zero phase. The enormous
interpretive benefits of zero phase data have been discussed in previous
Geophysical Corner columns ("Seismic/Geology Links Critical," AAPG
Explorer November 1996, also Search and Discovery Article #40130
(2004), and "Zero Phase Can Aid Interpretation," AAPG Explorer
April 1997, also in “Understanding the Seismic Wavelet,” by Steven G.
Henry, Search and Discovery Article #40028 (2001)).
Autocorrelations may be calculated over several time windows in an
attempt to allow for changes in the shape of the embedded wavelet as it
travels through the earth. This is called adaptive deconvolution.
shortens the embedded wavelet and attempts to make it as close as
possible to a spike. The frequency bandwidth of the data limits the
extent to which this is possible. This is also called whitening
deconvolution, because it attempts to achieve a flat, or white,
spectrum. This kind of deconvolution may result in increased noise,
particularly at high frequencies.
later portions of the autocorrelation to remove the effects of some
multiples. Predictability means that the arrival of an event can be
predicted from knowledge of earlier events. Different formulations are
used, including maximum and minimum entropy, a measure of disorder.
attempts to minimize the number of reflections, thus emphasizing large
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