bozhang-PhD / seismic_deep_learning

A couple of python script to extract geological structures from geophysical images using deep learning

Deep learning of geological structures from seismic reflection data

Here we are sharing our code, tutorials and examples used to interpret geological structures (e.g. faults, salt bodies and horizones) in 2-D and/or 3-D seismic reflection data using deep learning. You can find a few visual examples on our poster and more technical details in our preprint.

To get started, you don't need any special hardware, software, data or experience - just a bit of time. Check out tutorial-1/tutorial-1.ipyng.

Tutorials

Tutorial-1

• This tutorial shows you how to map salt in a 2-D seismic image using a 2-D convolutional neural network for pixel-wise classification.

Tutorial-2

• This tutorial describes how to speed up our mapping using U-Net type convolutional neural networks.

Tutorial-3

• This tutorial shows you how to map tectonic faults in a 3-D seismic volume.

Tutorial-4

• This tutorial will explain how to translate our whole workflow to 3-D.

Tutorial-5

• This tutorial will show you how we can map stratigraphic horizons in a 3-D seismic volume.

Citation

If you use this project in your research or wish to refer to the results of the tutorials, please use the following BibTeX entry.

@misc{deepseis2020,
  author =       {Thilo Wrona, Indranil Pan, Rebecca E. Bell, Robert L. Gawthorpe, Haakon Fossen and Sascha Brune},
  title =        {{Deep learning of geological structures in seismic reflection data: Tutorials}},
  howpublished = {\url{https://github.com/thilowrona/seismic_deep_learning}},
  year =         {2020}
}