We have truly entered the era of machine learning, and new and exciting models and techniques are developed and designed every day.

One of the challenges of ML, however, is the rich variety of formats and data around. It is very difficult to build machine learning models if it is difficult to get data.

SEG-Y has been an industry standard for over 40 years now, and data in SEG-Y files are quite valuable. Leveraging this data is a key component in many machine learning projects, and the format still represents a challenge.

This is a hands-on workshop describing and demonstrating practical use of the free Python library segyio (https://github.com/equinor/segyio) for reading and writing SEG-Y. It has become quite popular in the open geoscience community, and is designed from scratch to be a suitable building block for new applications.

The workshop will focus on dialogue and discussion, and address some common use cases for machine learning and implementing SEG-Y support in new machine learning and geoscience projects.


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  1. Ilstad, C.R.
    [2017] Malenov - machine learning of voxels. https://github.com/bolgebrygg/ MalenoV/. Accessed: 13-02-2019.
    [Google Scholar]
  2. Oliphant, T.
    [2006] Guide to NumPy.
    [Google Scholar]
  3. Yee, K. and van Rossum, G.
    [2001] PEP 234 – Iterators. https://www.python.org/dev/peps/pep-0234/. Accessed: 2019-02-13.
    [Google Scholar]

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