Deep Seismic Compression

References

1. Agustsson, E., Mentzer, F., Tschannen, M., Cavigelli, L., Timofte, R.
   , Benini, L. and Van Gool, L. [2017] Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations.
   [Google Scholar]
2. Averbuch, A.Z., Meyer, F., Stromberg, J.., Coifman, R. and Vassiliou, A
   . [2001] Low bit-rate efficient compression for seismic data. IEEE Transactions on Image Processing, 10(12), 1801–1814.
   [Google Scholar]
3. Li, M., Zuo, W., Gu, S., Zhao, D. and Zhang, D.
   [2017] Learning Convolutional Networks for Content-weighted Image Compression. arXiv preprint arXiv:1703.10553.
   [Google Scholar]
4. Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R. and Van Gool, L
   . [2018] Conditional Probability Models for Deep Image Compression. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
   [Google Scholar]
5. SEG
   [2019] Open data. http://wiki.seg.org/wiki/Open data.Accessed: 2019-01-10.
6. Theis, L., Shi, W., Cunningham, A. and Huszár, F
   . [2017] Lossy Image Compression with Compressive Autoencoders. In: International Conference on Learning Representations.
   [Google Scholar]
7. Van Oord, A., Kalchbrenner, N. and Kavukcuoglu, K
   . [2016] Pixel Recurrent Neural Networks. In: International Conference on Machine Learning. 1747–1756.
   [Google Scholar]
8. Zhang, Y., Da Silva, C., Kumar, R. and Herrmann, F.
   [2017] Massive 3D seismic data compression and inversion with hierarchical Tucker.
   [Google Scholar]