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http://dx.doi.org/10.9719/EEG.2020.53.4.479

Research Trend Analysis for Fault Detection Methods Using Machine Learning  

Bae, Wooram (Department of Energy Resources Engineering, Pukyong National University)
Ha, Wansoo (Department of Energy Resources Engineering, Pukyong National University)
Publication Information
Economic and Environmental Geology / v.53, no.4, 2020 , pp. 479-489 More about this Journal
Abstract
A fault is a geological structure that can be a migration path or a cap rock of hydrocarbon such as oil and gas, formed from source rock. The fault is one of the main targets of seismic exploration to find reservoirs in which hydrocarbon have accumulated. However, conventional fault detection methods using lateral discontinuity in seismic data such as semblance, coherence, variance, gradient magnitude and fault likelihood, have problem that professional interpreters have to invest lots of time and computational costs. Therefore, many researchers are conducting various studies to save computational costs and time for fault interpretation, and machine learning technologies attracted attention recently. Among various machine learning technologies, many researchers are conducting fault interpretation studies using the support vector machine, multi-layer perceptron, deep neural networks and convolutional neural networks algorithms. Especially, researchers use not only their own convolution networks but also proven networks in image processing to predict fault locations and fault information such as strike and dip. In this paper, by investigating and analyzing these studies, we found that the convolutional neural networks based on the U-Net from image processing is the most effective one for fault detection and interpretation. Further studies can expect better results from fault detection and interpretation using the convolutional neural networks along with transfer learning and data augmentation.
Keywords
fault detection; machine learning; support vector machine; deep neural networks; convolutional neural networks;
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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