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README.md

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-license: cc-by-nc-sa-4.0
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+---
+license: cc-by-nc-sa-4.0
+size_categories:
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+---
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+**OpenFWI: Large-scale Multi-structural Benchmark Datasets for Full Waveform Inversion**
+
+
+OpenFWI is the first collection of large-scale, multi-structural benchmark datasets for machine learning driven seismic Full Wave Inversion (FWI). It includes eleven 2D datasets and one 3D dataset, in four subsurface structure families. Here, We provide three of them (Vel, Fault, and Style). For both 2D and 3D Kimberlina datasets, please refer to [OpenFWI](https://smileunc.github.io/projects/openfwi) for more details.
+
+
+**Resources**
+
+[Paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/27d3ef263c7cb8d542c4f9815a49b69b-Paper-Datasets_and_Benchmarks.pdf) - Discover the technical details and baseline methods.
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+[Github Repo](https://github.com/lanl/OpenFWI) - Pytorch Implementation of OpenFWI Benchmarks.
+
+[Tutorial](https://www.kaggle.com/competitions/waveform-inversion/data)  - A simple example of using the data and our baseline models.
+
+[OpenFWI Website](https://openfwi-lanl.github.io) - Explore more resources on  the official website of OpenFWI.
+
+**License**
+
+This dataset is licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/) (CC BY-NC-SA 4.0).
+
+**Citation**
+
+If you find the data useful, please cite:
+```bibtex
+@article{deng2022openfwi,
+  title={OpenFWI: Large-scale multi-structural benchmark datasets for full waveform inversion},
+  author={Deng, Chengyuan and Feng, Shihang and Wang, Hanchen and Zhang, Xitong and Jin, Peng and Feng, Yinan and Zeng, Qili and Chen, Yinpeng and Lin, Youzuo},
+  journal={Advances in Neural Information Processing Systems},
+  volume={35},
+  pages={6007--6020},
+  year={2022}
+}
+```