Treffer: zfbi/rgtNet: Deep learning for simultaneously interpreting 3D seismic horizons and faults by estimating a relative geologic time volume

RgtNet:using synthetic datasets to train an end-to-end CNN for 3-D RGT(Relative Geologic Time) estimation This is a Pytorch version of RgtNet for 3-D RGT(Relative Geologic Time) estimation Getting Started with Example Model for RGT estimation If you would just like to try out a pretrained example model, then you can download the pretrained model [neuc] and use the demo.ipynb script to run a demo (example data can be downloaded from here). Requirments python>=3.6 torch>=1.0.0 torchvision torchsummary natsort numpy pillow plotly pyparsing scipy scikit-image sklearn tqdm Install all dependent libraries: pip install -r requirements.txt Dataset To train our CNN network, we automatically created 400 pairs of synthetic seismic and corresponding RGT volumes, which were shown to be sufficient to train a good RGT estimation network. The training and validation datasets can be downloaded here Training Run train.sh to start training a new RgtNet model by using the synthetic dataset sh train.sh Validation & Application Run infer.sh to start applying a new RgtNet model to the synthetic or field seismic data sh infer.sh License This extension to the Pytorch 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/