1 |
Di, H., Shafiq, M.A. and AlRegib, G. (2017). Seismic-fault detection based on multiattribute support vector machine analysis. In SEG Technical Program Expanded Abstracts 2017 (pp. 2039-2044). Society of Exploration Geophysicists.
|
2 |
Di, H., Shafiq, M.A., Wang, Z. and AlRegib, G. (2019). Improving seismic fault detection by super-attributebased classification. Interpretation, v.7(3), SE251-SE267.
DOI
|
3 |
Fehler, M. and Larner, K. (2008). SEG advanced modeling (SEAM): Phase I first year update. The Leading Edge, v.27(8), p.1006-1007.
DOI
|
4 |
Geron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow. O'Reilly Media, California, United States
|
5 |
Gersztenkorn, A. and Marfurt, K.J. (1999). Eigenstructurebased coherence computations as an aid to 3-D structural and stratigraphic mapping. Geophysics, v.64(5), p.1468-1479.
DOI
|
6 |
Guo, B., Liu, L. and Luo, Y. (2018, December). Automatic seismic fault detection with convolutional neural network. In International Geophysical Conference, Beijing, China, 24-27 April 2018 (pp. 1786-1789). Society of Exploration Geophysicists and Chinese Petroleum Society.
|
7 |
Hale, D. (2009). Structure-oriented smoothing and semblance. CWP report, 635(635).
|
8 |
Hale, D. (2013). Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images. Geophysics, v.78(2), O33-O43.
DOI
|
9 |
Huang, L., Dong, X. and Clee, T.E. (2017). A scalable deep learning platform for identifying geologic features from seismic attributes. The Leading Edge, v.36(3), p.249-256.
DOI
|
10 |
Di, H. (2018). Developing a seismic pattern interpretation network (SpiNet) for automated seismic interpretation. arXiv preprint arXiv:1810.08517.
|
11 |
Li, F. and Lu, W. (2014). Coherence attribute at different spectral scales. Interpretation, v.2(1), SA99-SA106.
DOI
|
12 |
Karimi, P., Fomel, S., Wood, L. and Dunlap, D. (2015). Predictive coherence: Interpretation, 3. SAE1-SAE7, http://dx. doi. org/10.1190/INT-2015-0030.1.
|
13 |
Kim, T.Y. and Yoon, W.J. (1999). Seismic Traveltime Tomography using Neural Network. Geophysics and Geophysical Exploration, v.2(4), p.167-173.
|
14 |
Lee, H. and Shin, C.H. (2019a). Investigation of Advanced Seismic Interpretation Using Machine Learning Technology. Proceedings of Fall Meeting, The Korean Institute of Gas, p.126-126.
|
15 |
Lee, H. and Shin, C.H. (2019b). Investigation of Quality Improvement Techniques of Seismic Data Using Machine Learning Technology. Proceedings of Fall Meeting, The Korean Institute of Gas, p.124-124.
|
16 |
Lee, H., Mo, C.H., Park S.S. and Shin, C.H. (2018). Methods to Improve the Quality of Seismic Data Using Machine Learning Techniques. Proceedings of Fall Meeting, The Korean Institute of Gas, p.151-151.
|
17 |
Li, S., Yang, C., Sun, H. and Zhang, H. (2019). Seismic fault detection using an encoder-decoder convolutional neural network with a small training set. Journal of Geophysics and Engineering, v.16(1), p.175-189.
DOI
|
18 |
Marfurt, K.J., Kirlin, R.L., Farmer, S.L. and Bahorich, M.S. (1998). 3-D seismic attributes using a semblancebased coherency algorithm. Geophysics, v.63(4), p.1150-1165.
DOI
|
19 |
Marfurt, K.J., Sudhaker, V., Gersztenkorn, A., Crawford, K.D. and Nissen, S.E. (1999). Coherency calculations in the presence of structural dip. Geophysics, v.64(1), p.104-111.
DOI
|
20 |
Hwang, H.S., Lee, S.K., Lee, T.S. and Sung, N.H. (2000). Minimisation Technique for Seismic Noise Using a Neural Network. Geophysics and Geophysical Exploration, v.3(3), p.83-87.
|
21 |
Wu, X. and Hale, D. (2016). 3D seismic image processing for faults. Geophysics, v.81(2), IM1-IM11.
DOI
|
22 |
Pochet, A., Diniz, P.H., Lopes, H. and Gattass, M. (2018). Seismic fault detection using convolutional neural networks trained on synthetic poststacked amplitude maps. IEEE Geoscience and Remote Sensing Letters, v.16(3), p.352-356.
DOI
|
23 |
Randen, T., Pedersen, S.I. and Sonneland, L. (2001). Automatic extraction of fault surfaces from threedimensional seismic data. In SEG Technical Program Expanded Abstracts 2001 (pp. 551-554). Society of Exploration Geophysicists.
|
24 |
Ronneberger, O., Fischer, P. and Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
|
25 |
Van Bemmel, P.P. and Pepper, R.E. (2000). Seismic signal processing method and apparatus for generating a cube of variance values., U.S. Patent No. 6,151,555.
|
26 |
Wu, X. (2017). Directional structure-tensor-based coherence to detect seismic faults and channels. Geophysics, v.82(2), p.A13-A17.
DOI
|
27 |
Wu, X., Liang, L., Shi, Y. and Fomel, S. (2019a). FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics, v.84(3), IM35-IM45.
DOI
|
28 |
Park, J., Yoon, D., Seol, S.J. and Byun, J. (2019). Reconstruction of seismic field data with convolutional UNet considering the optimal training input data. In SEG Technical Program Expanded Abstracts 2019 (pp. 4650-4654). Society of Exploration Geophysicists.
|
29 |
Wu, X., Shi, Y., Fomel, S., Liang, L., Zhang, Q. and Yusifov, A.Z. (2019c). FaultNet3D: predicting fault probabilities, strikes, and dips with a single convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, v.57(11), p.9138-9155.
DOI
|
30 |
Wu, X., Shi, Y., Fomel, S. and Liang, L. (2018). Convolutional neural networks for fault interpretation in seismic images. In SEG Technical Program Expanded Abstracts 2018 (pp. 1946-1950). Society of Exploration Geophysicists.
|
31 |
Xiong, W., Ji, X., Ma, Y., Wang, Y., AlBinHassan, N.M., Ali, M.N. and Luo, Y. (2018). Seismic fault detection with convolutional neural network. Geophysics, v.83(5), O97-O103.
DOI
|
32 |
Zhao, T. (2019). 3D convolutional neural networks for efficient fault detection and orientation estimation. In SEG Technical Program Expanded Abstracts 2019 (pp. 2418-2422). Society of Exploration Geophysicists.
|
33 |
Zhao, T. and Mukhopadhyay, P. (2018). A fault detection workflow using deep learning and image processing. In SEG Technical Program Expanded Abstracts 2018 (pp. 1966-1970). Society of Exploration Geophysicists.
|
34 |
Zheng, Z.H., Kavousi, P. and Di, H.B. (2014). Multiattributes and neural network-based fault detection in 3D seismic interpretation. In Advanced Materials Research (Vol. 838, pp. 1497-1502). Trans Tech Publications Ltd.
DOI
|
35 |
Zhou, R., Cai, Y., Yu, F. and Hu, G. (2019). Seismic fault detection with iterative deep learning. In SEG Technical Program Expanded Abstracts 2019 (pp. 2503-2507). Society of Exploration Geophysicists.
|
36 |
Wu, X., Liang, L., Shi, Y., Geng, Z. and Fomel, S. (2019b). Multitask learning for local seismic image processing: fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single convolutional neural network. Geophysical Journal International, v.219(3), p.2097-2109.
DOI
|
37 |
Chang, D., Yang, W., Yong, X. and Yang, Q. (2018, December). Seismic fault detection using deep learning technology. In International Geophysical Conference, Beijing, China, 24-27 April 2018 (pp. 1770-1773). Society of Exploration Geophysicists and Chinese Petroleum Society.
|
38 |
Aqrawi, A.A. and Boe, T.H. (2011). Improved fault segmentation using a dip guided and modified 3D Sobel filter. In SEG Technical Program Expanded Abstracts 2011 (pp. 999-1003). Society of Exploration Geophysicists.
|
39 |
Araya-Polo, M., Dahlke, T., Frogner, C., Zhang, C., Poggio, T. and Hohl, D. (2017). Automated fault detection without seismic processing. The Leading Edge, v.36(3), p.208-214.
DOI
|
40 |
Chang, D.K., Yang, W.Y., Yong, X.S., Li, H.S., Wang, Y.H. and Chen, D.W. (2019, December). Semantic segmentation network for 3D seismic fault system detection. In SEG 2019 Workshop: Fractured Reservoir & Unconventional Resources Forum: Prospects and Challenges in the Era of Big Data, Lanzhou, China, 1-3 September 2019 (pp. 113-116). Society of Exploration Geophysicists.
|
41 |
Choi, Y., Seol, S.J., Byun, J. and Kim, Y. (2019). Vertical resolution enhancement of seismic data with convolutional U-net. In SEG Technical Program Expanded Abstracts 2019 (pp. 2388-2392). Society of Exploration Geophysicists.
|
42 |
Cunha, A., Pochet, A., Lopes, H. and Gattass, M. (2020). Seismic fault detection in real data using transfer learning from a convolutional neural network pretrained with synthetic seismic data. Computers and Geosciences, .135, 104344.
DOI
|