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. The repository is organised in a series of tutorials (Jupyter notebooks) with increasing degree of difficulty. We show step-by-step how to: (1) load seismic data, (2) train a model and (3) apply the model to map different geological structures. 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.
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{deepseis2021,
author = {Thilo Wrona, Indranil Pan, Rebecca E. Bell, Robert L. Gawthorpe, Haakon Fossen and Sascha Brune},
title = {3-D seismic interpretation with deep learning: a set of Python tutorials},
doi = {10.5880/GFZ.2.5.2021.001},
url = {https://doi.org/10.5880/GFZ.2.5.2021.001},
year = {2021}
}