These blog posts will build up into a complete description of a 2D marine processing sequence. They are based on our tutorial datasets, which in turn came from the New Zealand Government’s Ministry of Economic Development under the “Open File” System.
The processing sequence we have developed so far is:
- apply a combined minimum phase conversion and anti-alias filter
- re-sample from 2 ms sample interval to 4 ms sample interval
- assign a simple straight line 2D marine geometry
- check the geometry by plotting offsets over the shots and near traces
- true amplitude recovery using a T2 spherical divergence correction
- trace edits using peak-amplitude analysis to identify noisy shots and channels
In this post I’m going to continue cleaning up the shot records, to get them ready for more signal processing, by addressing some of the noise issues we identified earlier.
Here’s a shot record with a swell noise burst, plotted alongside its FK response.
The swell noise shows up as low frequency (in this case less than 5 Hz) energy with a broad spatial frequency band (indicated in red). This overlaps with the tail-buoy jerk (white) which is also low frequency, but dips from the tail of the cable to the head. These often occur together, with liquid-filled seismic streamers; modern streamers achieve their neutral buoyancy through foam not oil. The motion of the tail-buoy over the sea swell sets up waves that propagate through the cable, creating the noise.
The direct and refracted arrivals are highly spatially aliased (purple) and wrap back over the reflected signals we want to preserve; the aliased data crosses the K=0 axis at about 62.5 Hz, which is at the high end of our frequency band. We can also see some periodicity in the FK plot indicating short period multiples, as well as the back dipping (tail-to-head) reflections caused by structure we extend below the K=0 line.
The combination of the XT and FK displays suggest approaches we can use to tackle these noise issues, the main one of which is swell noise.
- throw data away by editing the traces that have swell noise present
- address the swell noise and tail-buoy jerk by removing only those frequencies
- address the swell noise and tail-buoy jerk by muting them in the FK-domain; retaining the low frequency signal (along with some noise) around the K=0 axis
There is also another technique we can use called “projective filtering”; this takes a moving time- and space- window and looks at the frequency content, aiming to locate and scale back anomalous low frequency noise within a trace automatically.
Firstly I’ll define a window below the direct and refracted arrivals for the technique to be applied in. This will avoid introducing artefacts into the data, which can happen at sharp amplitude boundaries. Most software allows you to design a spatial application gate in this way, above (or below) which the process isn’t applied.
Secondly, when I go into the FK domain I’ll use a removable AGC which will also help to avoid any amplitude complications.
|FK Domain mute designed to remove swell noise and tail-buoy jerk, while preserving low frequency reflections. Data above the red line will be muted in the FK domain|
Both of these approaches will help to remove artefacts.
For the FK approach, the mute I have picked is designed to be above the direct and refracted arrivals, including the aliased components; we’ll worry about those later.
These are the four basic processes we can test; we could of course vary the low-cut filters, or modify how the FK mute was applied – however the projective filtering is very effective in this case.
It is also important to look at what is being removed from the data by calculating “difference” plots to make sure that we are not removing signal.
If we compare the band-pass filter approach to the projective filtering, we can see that this removes less signal:
|A single shot record with (from left to right) no swell noise applied, projective filtering, and the difference plot showing what has been removed. The swell noise is being attenuated, but without removing any of the reflection energy|
Finally, here are the FK plots from the input data and the same shot with projective filtering applied (as an additional check):
|The FK spectra of the input shot record (left) and after projective filtering (right); the signature of the swell noise and tail-buoy jerk (red) have been attenuated|
If you were testing this, in practice you would use more than one shot; a selection of shots from along the line, ideally ones that you have identified as having swell noise issues as part of the noise QC tests you have run so far.
In this case I’ll continue processing using the projective filtering. If you didn’t have this available then varying the filter (or the frequency “cut” for the FK mute) can increase how harshly the swell noise is attenuated, but of course can cost you signal.
One of the techniques you can employ is to select just the traces with significant swell noise for the harshest filtering. In our software you can store the peak amplitude in a trace header and then select traces where this exceeds a given value.
Now we have de-spiked and “clean” shot records. The next thing we need to address is the strong linear noise from the direct and refracted arrivals.
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