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| A new bidirectional deconvolution method that overcomes the minimum phase assumption | |
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In the previous report (Zhang and Claerbout, 2010), we introduced the spiking
deconvolution problem using the hybrid norm solver
(Claerbout, 2009a). Synthetic examples (Zhang and Claerbout, 2010)
showed that given a minimum-phase wavelet, it retrieved the sparse
reflectivity model almost perfectly even with a reflection series that is
far from white, while conventional L2
deconvolution did a poor job. However, if the assumption of a
minimum-phase wavelet was removed, the hybrid norm spiking deconvolution
failed quickly and gave a poor result similar to the conventional L2
deconvolution.
In this paper, we still rely on the hybrid norm solver to retrieve the
sparse model, but we use a slightly more complex formulation that
avoids the minimum-phase wavelet constraint.
We start by realizing that any (mixed-phase) wavelet
can be
decomposed into a minimum-phase part
and a maximum-phase part
plus a certain time shift:
|
(1) |
where
is also a minimum-phase wavelet (therefore
is
a maximum-phase wavelet) and the exponent
is the order of
. This
term makes the wavelet
causal. In the time domain,
(1) can be written as
|
(2) |
where
stands for the time reverse of series
.
Our original spiking deconvolution can find only a minimum-phase wavelet which
has the same spectrum of real wavelet
. It can be defined as an inverse
problem as follows:
|
(3) |
where
is the data convolution operator, and
is the
unknown filter. In this formulation, the filter is the only unknown,
the hybrid norm is applied on the residual term
to enforce the
sparseness constraint. In theory, the residual
itself is the
reflectivity model. Such a method requires the wavelet in the data
to be minimum-phase because only a minimum-phase wavelet has a
causal stable inverse.
The following bidirectional deconvolution formulation utilizes a pair of conventional
deconvolutions, trying to invert components
and
separately:
|
(4) |
in which
and
are the corresponding filters that corresponds to the
inverses of
and
denoted above, the superscript
means
time-reverse. The operator in each equation is the convolution
operator. Again the hybrid norm is applied to
and
, and the
reflectivity model is simply
plus a time shift. Notice that
this is a non-linear inversion, since the operator itself depends on
the unknown
and
. In practice we have to solve these two
inversions alternately and therefore iteratively.
To understand the meaning of (4), let
|
(5) |
where m is the reflectivity model and the
term is just a
time shift. Assume
and
are perfectly known in the
operators (which is not true in reality), i.e.
Substituting (5) into (4), since
we have
|
(8) |
From (8) it is easier to see what is behind the bidirectional deconvolution
formulation (4): It tries to separate the two parts of the
wavelet, turning each one into a traditional deconvolution problem
in which the wavelet (
) is always minimum-phase.
As with all non-linear estimation, iteration is required. Convergence is assured
if the starting solution is close enough. We expect the traditional PEF for
and an
impulse function for
to be a pretty good first guess. The following section shows
several examples (complexity varies from low to high) illustrating the effectiveness
and limitations of the method.
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| A new bidirectional deconvolution method that overcomes the minimum phase assumption | |
|
Next: Data Examples
Up: Zhang and Claerbout: Hybrid
Previous: Zhang and Claerbout: Hybrid
2010-11-26