FaultSeg3D plus: A comprehensive study on evaluating and improving CNN-based seismic fault segmentation

Here we provide both Tensorflow and Pytorch versions of FaultSegPlus (improved from faultSeg by Wu et al. (2019)) for 3D fault segmentation

As described in FaultSeg3D plus: A comprehensive study on evaluating and improving CNN-based seismic fault segmentation by You Li¹, Xinming Wu^*,1, Zhengyu Zhu, Jicai Ding, and Qingzhen Wang ¹School of Earth and Space Sciences, USTC; ²CNOOC.

Getting Started with Example Model for fault prediction

If you would just like to try out a pretrained example model, then you can download the pretrained model and use the 'apply.py' script to run a demo.

Dataset

To train our CNN network, we automatically created 400 pairs of synthetic seismic and corresponding fault volumes, which were shown to be more realistic and diverse than the 200 pairs published by Wu et al. (2019).

The training and validation datasets will be uploaded soon

Training

Run train.py to start training a new faultSeg model by using the 400 synthetic datasets

Publications

If you find this work helpful in your research, please cite:

@article{li2025faultSegPlus,
    author = {You Li and Xinming Wu and Zhenyu Zhu and Jicai Ding and Qingzhen Wang},
    title = {Fault{S}eg3{D} plus: A comprehensive study on evaluating and improving CNN-based seismic fault segmentation},
    journal = {GEOPHYSICS},
    volume = {89},
    number = {5},
    pages = {N77–N91},
    year = {2024},
}

License

This extension to the Keras library is released under a creative commons license which allows for personal and research use only. For a commercial license please contact the authors. You can view a license summary here: http://creativecommons.org/licenses/by-nc/4.0/