3D Convolutional Neural Networks for Fault Interpretation

References

1. Guitton, A., Wang, H. and Trainor-Guitton, W.
   [2017] Statistical imaging of faults in 3D seismic volumes using a machine learning approac. In: SEG Technical Program Expanded Abstracts2017. 2045–2049.
   [Google Scholar]
2. Hale, D.
   [2013] Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images. Geophysics, 78(2), O33–O43.
   [Google Scholar]
3. He, K., Zhang, X., Ren, S. and Sun, J.
   [2015] Deep Residual Learning for Image Recognition. CoRR, abs/1512.03385.
   [Google Scholar]
4. Ioffe, S. and Szegedy, C.
   [2015] Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. CoRR, abs/1502.03167.
   [Google Scholar]
5. Lomask, J., Guitton, A., Fomel, S., Claerbout, J. and Valenciano, A.A.
   [2006] Flattening without picking. Geophysics, 71(4), P13–P20.
   [Google Scholar]
6. Marfurt, K.J., Kirlin, R.L., Farmer, S.L. and Bahorich, M.S.
   [1998] 3-D seismic attributes using a semblance-based coherency algorithm. Geophysics, 63(4), 1150–1165.
   [Google Scholar]
7. Pedersen, S.I., Skov, T., Randen, T. and Sønneland, L.
   [2005] Automatic Fault Extraction Using Artificial Ants. in Mathematical Methods and Modelling in Hydrocarbon Exploration and Production, Springer Berlin Heidelberg, 107–116.
   [Google Scholar]
8. Simonyan, K. and Zisserman, A.
   [2014] Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, abs/1409.1556.
   [Google Scholar]
9. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R.
   [2014] Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929–1958.
   [Google Scholar]
10. Stark, T.J.
    [2004] Relative geologic time (age) volumes-Relating every seismic sample to a geologically reasonable horizon. The Leading Edge, 23(9), 928–932.
    [Google Scholar]
11. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. and Wojna, Z.
    [2015] Rethinking the Inception Architecture for Computer Vision. CoRR, abs/1512.00567.
    [Google Scholar]