The processing sequence we have developed so far gives us the
ideal input for predictive (or gap)deconvolution; it is minimum
phase, has the swell noise and strong amplitude linear noise
removed, and much of the spatially aliased high frequency dipping
events have been eliminated as well.
On marine datasets, the main goal of predictive deconvolution is
in part to collapse any residual effects caused by the limitations
of the tuned airgun array, and to help suppress short-period
reverberations in the wavelet. These reverberations occur mainly
from energy that has multiple reflections from the sea-surface and
seafloor, but they can also be from inter-bed multiples if there
are strong reflectors that are close together.
In this dataset I suspect there are also some mode converted
energy specifically P-S-P mode conversions where, especially in the
high shot point end of the line, the basement overthrust creates
the right conditions for this to happen.The basic tool we have for
looking at the reverberations in a dataset is the autocorrelation
function. It uses a sliding window of fixed length window to
mathematically compare the trace with itself, often over a specific
data range. The autocorrelation function is always symmetrical
about time zero where there is a strong peak. Subsequent peaks
indicate where a time-shifted version of the trace is similar to
the original.
Shots from the start and end of the line with an
auto-correlation appended to the bottom. The design window for the
autocorrelation function is indicated between the blue and yellow
lines
When working with deconvolution, this kind of display should be
your standard approach. Ive used a bit of trickery here in that I
have reduced the record length to 5500ms (for display purposes) and
then extended it by 100ms to create a gap between the shots and
their autocorrelations.
For the design window, I have defined the start gate using a
calculation based on offset (using the speed of sound in water as
1500ms-1and shifting this down by 200ms), and then made the gate
length 2500ms.
You can define the gates manually, but on marine data I prefer
to create a gate that is tied to offset and, if needed, shifted by
the water bottom. In doing so, if you see an anomalous result, it
is easier to back-track and adjust and of course on large
multi-line projects its less work.The design gate needs to: be at
least 5-7 times the length of the auto-correlation avoid any very
strong reflections usually just the seafloor, but there can be
others contain reflections if you cant see reflections in the gate
window, you will get a bad resultIn this case Ive got an
autocorrelation length of 300ms which should be enough to show the
reverberations caused by the water bottom (at about 80ms); note how
reverberant the data is on SP900.
The reason to focus on the autocorrelation is that it is not
just a quality control parameter it is also used to design the
deconvolution operator we will apply.
You can use more complex designs such as having multiple design
windows one above and one below a strong unconformity but the
problem can become this limits the design window and hence the
autocorrelation length that is viable. A long autocorrelation gives
a more stable result!
The other key parameter, as well as the length of the operator
(defined in turn by the autocorrelation), is the predictive gap. In
this case, we are not aiming to do much in the way of wavelet
shaping or whitening, so a longer multi-sample gap is preferable to
a short one.
This is where things become very subjective. Some people have
strong views on the gap being tied to particular values, or to the
first or second zero crossing of the auto-correlation function and
so on however all deconvolution code is different, and my advice is
to *always* test the gap.
There are three basic approaches to deconvolution we need to
test: we can work one trace at a time, in the X-T domain we can
average autocorrelation functions over multiple traces, or even a
shot we can apply deconvolution in the Tau-P domainThe first of
these is the usual marine work horse, but in situations where the
data is noisy the trace-averaging approach can be effective. Tau-P
domain deconvolution is a special case, as well discuss later.
For the XT domain approaches, I generally start with operator
tests using a 24ms gap; I run these from about 1.5x the first peak
on the autocorrelation function up to the largest value that makes
sense given the design criteria. In this case I might look at
150ms, 250ms and 300ms.Once I have an operator, I then test gaps
usually 8ms, 16ms, 24ms 32ms and 48ms, perhaps with a spiking (one
sample) gap as well.The results tend to be pretty subjective, and
depend on the interpreters needs, but 24ms is a fairly standard
choice.
Im not going to fill this post with images of different
deconvolution test panels on shots and stacks you can see those
inYilmaz(you should probably have access to a copy, Ive never
worked anywhere that didnt have one available).
Shots from the start and end of the line; a 24ms gap, 300ms
operator XT domain deconvolutionapplied. Start/end design gates
displayed (blue, yellow lines)
Tau-P domain deconvolution is a little different. It is based on
the idea that the multiples are more periodic in the Tau-P domain
than in X-T, but has the additional advantage that you dont have
the same restriction on design gate lengths at far offsets and
hence can have a longer, more stable operator.
The design process is the same as with the X-T domain, but in
general a longer gap (32ms or 48ms) works better. In general, Tau-P
domain deconvolution is a lot more effective that X-T domain.
In this case Ive tested operators from 400ms to 500ms, and gaps
of 24ms, 32ms and 48ms. These tests are a lot slower to run, of
course.
In practice the 500ms operator and 32ms gap gave the best
result.
Shot record from start and end of the line with no
deconvolution
Shot record from start and end of the line with 24ms gap, 300ms
operator XT deconvolution
Shot record from start and end of the line with 32ms gap, 500ms
operator Tau-P deconvolution
In practice, the differences are relatively minor between the
X-T and Tau-P domain deconvolution results. This is partly because
we have already applied Tau-P domain linear noise suppression,
which can have a big impact on how effective the deconvolution
is.
Ultimately the choice of what to use depends on the time and
resources you have available the Tau-P domain deconvolution is
computationally expensive, but if you are using Tau-P domain linear
noise suppression, these methods can be combined at that
stage.Running a second deconvolution on common receiver gathers can
also help improve the effectiveness of the result; if you have used
shot-ensemble or Tau-P domain deconvolution in the first pass.Its
also important to review stacked sections either the entire line or
just key areas with these tests, to ensure that the results on the
stacks match what you require.
Stacked section with: constant velocity, stretch mute, amplitude
recovery and swell noise recovery (no deconvolution)
Stacked section from above with Tau-P domain muting and
deconvolution applied