Between 2 and 3 years ago I started turning my long time passion for image processing, and particularly morphological image processing, to the task of fault segmentation.
At the time I shared my preliminary code, of which I was very happy, in a Jupyter notebook, which you can run interactively at this GitHub repository.
Two areas need improvement to get that initial workflow closer to a production one. The first one is on the image processing and morphology side; I am thinking of including: a better way to clean-up very short faults; pruning to eliminate spurious segments in the skeletonization result; the von Mises distribution instead of standard distribution to filter low dip angles. I know how to improve all those aspects, and have some code snippets sitting around in various locations on my Mac, but I am not quite ready to push for it.
The second area, on the seismic side, is ability to work with 3D data. This has been a sore spot for some time.
Enter segyio, a fast, open-source library, developed precisely to work with SEGY files. To be fair, segyio has been around for some time, as I very well know from being a member of the Software Underground community (swung), but it was only a month or so ago that I started tinkering with it.
This post is mostly to share back with the community what I’ve learned in my very first playground session (with some very helpful tips from Jørgen Kvalsvik, a fellow member of swung, and one of the creators of segyio), which allowed me to create a 3D fault segmentation volume (and have lots of fun in the process) from a similarity (or discontinuity) volume.
The workflow, which you can run interactively at this segyio-notebooks GitHub repository (look for the 01 – Basic tutorial) is summarized pictorially in Figure 1, and comprises the steps below:
- use
segy-ioto import two seismic volumes in SEGY file format from the F3 dataset, offshore Netherlands, licensed CC-BY-SA: a similarity volume, and an amplitude volume (with dip steered median filter smoothing applied) - manipulate the similarity to create a discontinuity/fault volume
- create a fault mask and display a couple of amplitude time slices with superimposed faults
- write the fault volume to SEGY file using
segy-io, re-using the headers from the input file
Figure 1. Naive 3D seismic fault segmentation workflow in Python.
Get the notebooks on GitHub (look for the 01 – Basic tutorial)
Feedback is welcome.
DISCLAIMER: The steps outlined in the tutorial are not intended as a production-quality fault segmentation workflow. They work reasonably well on the small, clean similarity volume, artfully selected for the occasion, but it is just a simple example.