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A couple of python scripts to interpret geological structures from geophysical images using deep learning
| Date | Stars |
|---|---|
| 2026-07-31 | 264 |
| 2026-08-06 | 263 |
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# 3-D seismic interpretation with deep learning: a set of Python tutorials
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](https://www.thilowrona.com/posters) and more technical details in our [preprint](https://eartharxiv.org/repository/view/1683/).
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](https://github.com/thilowrona/seismic_deep_learning/blob/master/tutorial-1/tutorial-1.ipynb).
# Tutorials
## Salt
### [Tutorial 1](https://github.com/thilowrona/seismic_deep_learning/blob/master/tutorial-01/tutorial-1.ipynb)
- 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](https://github.com/thilowrona/seismic_deep_learning/blob/master/tutorial-02/tutorial-2.ipynb)
- This tutorial describes how to speed up our mapping using U-Net type convolutional neural networks.
## Faults
### [Tutorial 3](https://github.com/thilowrona/seismic_deep_learning/blob/master/tutorial-03/tutorial-3.ipynb)
- This tutorial shows you how to map tectonic faults in a 3-D seismic volume.
### [Tutorial 4](https://github.com/thilowrona/seismic_deep_learning/blob/master/tutorial-04/tutorial-4.ipynb)
- This tutorial will explain how to translate our fault mapping workflow to 3-D.
### [Tutorial 5](https://github.com/thilowrona/seismic_deep_learning/blob/master/tutorial-05/tutorial-5.ipynb)
- This tutorial will hows you how to quantify uncertainty during fault mapping (not ready yet)
## Horizons
### [Tutorial 6](https://github.com/thilowrona/seismic_deep_learning/blob/master/tutorial-06/tutorial-6.ipynb)
- This tutorial introduces you to mapping horizons in a 3-D seismic volume.
### [Tutorial 7](https://github.com/thilowrona/seismic_deep_learning/blob/master/tutorial-07/tutorial-7.ipynb)
- This tutorial shows you how to improve horizon mapping using a 2-D CNN
### [Tutorial 8](https://github.com/thilowrona/seismic_deep_learning/blob/master/tutorial-08/tutorial-8.ipynb)
- This tutorial will explain how to translate our horizon mapping workflow to 3-D.
## Acoustic impedance
### [Tutorial 9](https://github.com/thilowrona/seismic_deep_learning/blob/master/tutorial-09/tutorial-9.ipynb)
- This tutorial will show how to invert synthetic seismic data for rock properties (not ready yet)
# 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{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}
}
```
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