What is acquisition footprint noise in seismic data?

Acquisition footprint is a noise field that appears on 3D seismic amplitude slices or horizons as an interwoven linear crosshatching parallel to the source line and receiver line directions. It is for the most part an expression of inadequate acquisition geometry, resulting in insufficient sampling of the seismic wave field (aliasing) and irregularities in the offset and azimuth distribution, particularly in the cross line direction.

Sometimes source-generated noise and incorrect processing (for example residual NMO due to erroneous velocity picks, incomplete migration, or other systematic errors) can accentuate the footprint.

This noise can interfere with the mapping of stratigraphic features and fault patterns, posing a challenge to seismic interpreters working in both exploration and development settings.

To demonstrate the relevance of the phenomenon I show below a gallery of examples from the literature of severe footprint in land data: an amplitude time slice (Figure 1a) and a vertical section (Figure 1b) from a Saudi Arabian case study, some seismic attributes (Figures 2, 3, 4, and 5), and also some modeled streamer data (Figure 6).

Bannagi combo

Figure 1. Amplitude time slice (top, time = 0.44 s) showing footprint in both inline and crossline direction, and amplitude section (bottom) highlighting the effect in the vertical direction. From Al-Bannagi et al. Copyrighted material.

Penobscop_sobel

Figure 2. Edge detection (Sobel filter) on the Penobscot 3D horizon (average time ~= 0.98 s) displaying N-S footprint. From Hall.

F3_shallow_sobel

Figure 3. Edge detection (Sobel filter) on a shallow horizon (average time ~= 0.44 s)  from the F3 Netherlands 3D survey displaying E-W footprint.

Davogustto and Marfurt

Figure 4. Similarity attribute (top , time = 0.6 s), and most positive curvature (bottom, time = 1.3 s), both showing footprint. From Davogustto and Marfurt. Copyrighted material.

Chopra-Larsen

Figure 5. Amplitude time slice (top, time = 1.32 s) the corresponding  coherence section  (bottom) both showing footprint. From Chopra and Larsen. Copyrighted material.

Long et al

Figure 6. Acquisition footprint in the form of low fold striation due to dip streamer acquisition. From Long et al. Copyrighted material.

In my next post I will review (with more examples form literature) some strategies available to either prevent or minimize the footprint with better acquisition parameters and modeling of the stack response; I will also discuss some ways the footprint can be attenuated after the acquisition of the data (with bin regularization/interpolation, dip-steered median filters, and kx ky filters, from simple low-pass to more sophisticated ones) when the above mentioned strategies are not available, due to time/cost constraint or because the interpreter is working with legacy data.

In subsequent posts I will illustrate a workflow to model synthetic acquisition footprint using Python, and how to automatically remove it in the Fourier domain with frequency filters, and then how to remove it from real data.

References

Al-Bannagi et al. 2005 – Acquisition footprint suppression via the truncated SVD technique: Case studies from Saudi Arabia: The Leading Edge, SEG, 24, 832– 834.

Chopra and Larsen,  2000 – Acquisition Footprint, Its Detection and Removal: CSEG Recorder, 25 (8).

Davogusto and Martfurt, 2011 – Footprint Suppression Applied to Legacy Seismic Data Volumes: 31st Annual GCSSEPM Foundation Bob F Perkins Research Conference 2011.

F3 Netherlands open access 3D:  info on SEG Wiki

Hall, 2014 –  Sobel filtering horizons (open source Jupyter Notebook on GitHub).

Long et al., 2004 – On the issue of strike or dip streamer shooting for 3D multi-streamer acquisition: Exploration Geophysics, 35(2), 105-110.

Penobscot open access 3D:  info on SEG Wiki

4 responses to “What is acquisition footprint noise in seismic data?

  1. Excellent piece Matteo. As a geologist, how often do these noises get ‘resolved’ as structural elements, in your experience?

    • Thanks for the feedback Pablo. What I would say is that I have seen several cases of this noise making it all the way to the result of ant tracking, or fault likelihood, either generated on an interpreter’s workstation or, more concerning, delivered by service providers. I am not too sure if they end up ‘resolved’ as structural elements but since those volumes are often the ones where faults are automatically (or semiautomatically) picked, it will certainly be a tedious task to separate the genuine faults to go into a 3D geo-model form the spurious ones. I can see a number of approaches to deal with this, in revers order of desirability: 1 – do the tedious work; 2 – run the ant tracking with a directional reject filter, but this will possibly remove also small scale faults of the same orientation; 3 – filter out the footprint from the discontinuity or curvature volume to be the input for the ant tracking, but unless one uses good dip-steered, edge preserving structural smoothing, this will have some effects on the result too (depending on the amount of smoothing); 4 – remove the footprint with well designed, signal preserving frequency filters in the Fourier domain; 5 – better sample the seismic wave field. What do you think?

  2. Usually I see this effect diminish within the first 300-500 ms or so of land data (depending on the geometry), so your first examples (figure 1) is really severe! I presume these cross-hatched patterns are directly proportional to the effective fold? I mean if you showed the fold (after the top and bottom mutes) would it look stripey in cross-section like your figure 1?

    • I am totally with you Evan on the fact that the effect is often less visible at larger times, partly as a result of less irregular fold. But not always: so I added the two-way-times to all the figures, to show that in same of these cases the effect is still quite strong at large time values. Also, even when it seems to be less pronounced, it is likely still present (I think part of the reason, in addition to better fold, is the natural increase of wavelength with depth, so the stripes are more “spread-over”) and if still there it is likely to affect significantly the edge detection ad curvature – type attributes. I am hoping to show this with a bit of work on the open access data in future posts.

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