Automatic Salt Detection with Machine Learning

References

1. Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Sakkis, G., Spyropoulos, C.D. and Stamatopoulos, P.
   [1994] Learning to filter spam e-mail: A comparison of a naive Bayesian and a memory-based approach. In: Proceedings of the Workshop on Machine Learning and Textual Information Access. 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, 1–13.
   [Google Scholar]
2. Berthelot, A., Solberg, A.H. and Gelius, L.J.
   [2013] Texture attributes for detection of salt. Journal of Applied Geophysics, 88, 52–69.
   [Google Scholar]
3. Bobadilla, J., Ortega, F., Hernando, A. and Gutierrez, A.
   [2013] Recommender systems survey. Knowledge-Based Systems, 46, 109–132.
   [Google Scholar]
4. Hall, B.
   [2016] Facies classification using machine learning. The Leading Edge, 35, 906–909.
   [Google Scholar]
5. Halpert, A.D., Clapp, R.G. and Biondi, B.
   [2014] Salt delineation via intrepreter-guided 3D seismic image segmentation. Interpretation, 2, T79–T88.
   [Google Scholar]
6. Jia, Y. and Ma, J.
   [2017] What can machine learning do for seismic data processing? An interpolation application. Geophysics, 82, V163–V177.
   [Google Scholar]
7. Lim, J.S.
   [2005] Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea. Journal of Petroleum Science and Engineering, 49, 182–192.
   [Google Scholar]
8. Lomask, J., Clapp, R.G. and Biondi, B.
   [2007] Application of image segmentation to tracking 3D salt boundaries. Geophysics, 72, P47–P56.
   [Google Scholar]
9. Long, J., Shelhamer, E. and Darrell, T.
   [2015] Fully Convolutional Models for Semantic Segmentation, arXiv:1411.4038.
   [Google Scholar]
10. Ramirez, C., Larrazabal, G. and Gonzalez, G.
    [2016] Salt body detection from seismic data via sparse representation. Geophysical Prospecting, 64, 335–347.
    [Google Scholar]
11. Ronneberger, O., Fischer, P. and Brox, T.
    [2015] U-Net: Convolutional Networks for Biomedical Image Segmentation, arXiv:1505.0459v1.
    [Google Scholar]
12. Wang, Z., Hegazy, T., Long, Z. and AlRegib, G.
    [2015] Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensor and subspace leaning. Geophysics, 80, WD101–WD116.
    [Google Scholar]
13. Wu, X.
    [2016] Methods to compute salt likelihoods and extract salt boundaries from 3D seismic images. Geophysics, 81, IM119–IM126.
    [Google Scholar]
14. Zeng, H.
    [2004] Seismic geomorphology-based classification. The Leading Edge, 23, 644–688.
    [Google Scholar]
15. Zhang, C., Frogner, C., Araya-Polo, M. and Hohl, D.
    [2014] Machine-learning based automated fault detection in seismic traces. In: Extended Abstracts. EAGE, 807–811.
    [Google Scholar]
16. Zhao, T., Jayaram, V. and Marfurt, K.J.
    [2015] A comparison of classification techniques for seismic facies recognition. Interpretation, 3, SAE29–SAE58.
    [Google Scholar]
17. Zhou, J., Zhang, Y., Chen, Z. and Li, J.
    [2007] Detecting boundary of salt dome in seismic data with edge-detection technique. In: Expanded Abstracts. SEG, 1392–1396.
    [Google Scholar]