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.


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


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.


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.


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

Simulating seismic surveys using King Tut’s CAT scan

The remote sensing used to study the human body is very similar to the remote sensing used to study the subsurface. Apart from a scaling factor (due to the different frequencies of the signals used) the only major difference between the two methods of investigation is in that radiologists and doctors looking at an x-ray, ultrasound, or CAT scan image know what to look for in those images, as bones, tissues, and anomalies, have known characteristics, whereas the subsurface is always to a large extent unknown.

In this short visual post I am going to use a CAT scan of King Tut’s skull to explore the effect on the image quality of progressive decimation of the data followed by upsampling it back to the initial size. I will also look at the effect of these manipulations on the results of edge detection.

With this I want to simulate the progressive reduction in imaging quality that happens when going from high density 3D seismic acquisition to medium density 3D seismic to high quality, but sparse 2D seismic lines.

Here’s the input image in Figure 1.


Figure 1. CAT scan of King Tut’s skull – Supreme Council of Antiquities. guardians.net/hawass/press_release_tutankhamun_ct_scan_results.htm


In Figure 2 I am showing the image after import into a Jupiter Notebook and conversion to grayscale, and the result of edge detection using the Sobel filter. Notice the excellent quality of the edge detection result.


Figure 2. Original image, or ground truth for the experiment,  and edge detection result.

To simulate a high-resolution 3D seismic acquisition I decimated the original image by a factor of 4 in both directions. The resulting image (no interpolation) is, shown in Figure 3, is of good quality, and so is the edge detection result.


Figure 3. Simulated high-resolution 3D survey and edge detection result.

The image in Figure 4 results from a further decimation by a factor of 2 of the image in Figure 3, then interpolation to upsample to the same size as the image in Figure 4. The image and the edge detection are still of fair quality overall, but some of the smaller features have either disappeared, merged, or faded.

Figure 4. Simulated medium resolution 3D survey and edge detection result.

Figure 4. Simulated medium resolution 3D survey and edge detection result.

Now look at Figure 5: this is the equivalent of a high quality (in one direction) 2D dataset. Although we can still guess at what this represents, I would argue this is a result of our a priori knowledge of what it is supposed to represent – a human skull; and yet I don’t think anybody would want their doctor to make a diagnosis  based on this image.

Figure 5. Simulated set of very high-resolution 2D lines.

Figure 5. Simulated set of very high-resolution 2D lines.

The image in Figure 6 results from 2D interpolation (my intention is to simulate the result we would get by gridding 2D data to get a continuous image. We can now definitely interpret this as a skull, but the edge detection result is very unsatisfactory.

Figure 6. Simulated interpolation of 2D lines.

Figure 6. Simulated interpolation of 2D lines.

In  future post we will explore the effects of adding periodic noise (similar to seismic acquisition footprint) on these images and on the edge detection results. I will also show you how to remove it using 2D FFT filters, as promised (now more than a year ago) in my post Moiré Patterns.

If you would like to play with the code, get the Jupiter Notebook here.

Moiré Patterns

Moiré pattern

Some time ago I reblogged a post from El Ojo Inoportuno showing Moiré pattern, which resulted from taking a photo of a circular pattern of (beautiful) tiles. This phenomenon is caused by undersampling and is also called space aliasing. There’s a very good explanation of space aliasing and another stunning Moiré example on Agile Geoscience’s post N is for Nyquist.

Creating Moiré patterns

One way to get Moiré pattern is to superimpose two identical, transparent line gratings and rotate one by an angle. You can see an animation of this on Wolfram Mathworld here; notice that the pattern varies with the angle. In the same page there’s also an example of Moiré Patterns generated by plotting series of curves on a computer screen, which is very similar to taking the photo of circular tiles shown in the Ojo Inoportuno photo. Again the interference is caused by representing circles with a finite size pixel grid. If you are interested you can experiment with these effects and many more by downloading templates from this site. Figure 1 shows my own Moiré from circular patterns.


Figure 1


There is a program for interactive Moiré pattern experiments called iMoiré.

Another way to get a Moiré pattern is to scan a picture printed with halftone. There’s a simple explanation of this scanning-generated interference here. Again this is a matter of aliasing, or undersampling. Here’s a good example:

Figure 2

The original image is a lovely watercolor by  Ettore Roesler Franz showing medieval houses along the Tiber river in Rome. The Moiré Pattern results from scanning the watercolor from one of the book collections (the image was posted on Flickr here).

How to remove Moiré pattern from digital images

For a quick solution, there’s a good article with detailed instructions on how to remove Moiré pattern in Photoshop, Paint Shop Pro, etcetera. For a more advanced workflow there’s an excellent top hat filter in Photoshop included in Reindeer Graphic’s FoveaPro plugin. In Figure 3, I created a sort of pictorial chart of this workflow using low resolution copies of examples in The Image Processing Cookbook, by John C. Russ.


Figure 3


In future posts I plan to show how to remove Moire’ pattern with open source code images  using Python, and then to extend the workflow to the removal (or attenuation) of acquisition footprint in seismic data, which has a very similar appearance in the 2D Fourier domain, and can be filtered with very similar techniques.