Fault Detection

Faults are controlling pathways of hydrocarbon migration and accumulation in the subsurface. Hence, detection of faults on 3D seismic images is an important interpretation task in petroleum exploration and production.

Used ML Approaches:

• Support Vector Machine

• Artificial Neural Networks

Used Neural Networks:

• FC

• CNN

• GAN

• U-Net

• Siamese

• Autoencoder

• SegNet

Used Learning Procedures:

• Supervised Learning

• Semi-Supervised Learning

• Transfer Learning

• Ensemble Learning

References:

1. Di, H., & Gao, D. (2017). 3D seismic flexure analysis for subsurface fault detection and fracture characterization. Pure and Applied Geophysics, 174(3), 747-761.

2. Zheng, Z. H., Kavousi, P., & Di, H. B. (2014). Multi-attributes and neural network-based fault detection in 3D seismic interpretation. In Advanced Materials Research (Vol. 838, pp. 1497-1502). Trans Tech Publications Ltd.

3. Araya-Polo, M., Dahlke, T., Frogner, C., Zhang, C., Poggio, T., & Hohl, D. (2017). Automated fault detection without seismic processing. The Leading Edge, 36(3), 208-214.

4. Huang, L., Dong, X., & Clee, T. E. (2017). A scalable deep learning platform for identifying geologic features from seismic attributes. The Leading Edge, 36(3), 249-256.

5. Lu, P., Morris, M., Brazell, S., Comiskey, C., & Xiao, Y. (2018). Using generative adversarial networks to improve deep-learning fault interpretation networks. The Leading Edge, 37(8), 578-583.

6. Xiong, W., Ji, X., Ma, Y., Wang, Y., AlBinHassan, N. M., Ali, M. N., & Luo, Y. (2018). Seismic fault detection with convolutional neural network. Geophysics, 83(5), O97-O103.

7. Li, S., Yang, C., Sun, H., & Zhang, H. (2019). Seismic fault detection using an encoder–decoder convolutional neural network with a small training set. Journal of Geophysics and Engineering, 16(1), 175-189.

8. Wu, X., Liang, L., Shi, Y., & Fomel, S. (2019). FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics, 84(3), IM35-IM45.

9. Wu, X., Shi, Y., Fomel, S., Liang, L., Zhang, Q., & Yusifov, A. Z. (2019). FaultNet3D: Predicting fault probabilities, strikes, and dips with a single convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 57(11), 9138-9155.

10. Cunha, A., Pochet, A., Lopes, H., & Gattass, M. (2020). Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data. Computers & Geosciences, 135, 104344.

11. Dell, S., Walda, J., Hoelker, A., & Gajewski, D. (2020). Categorizing and correlating diffractivity attributes with seismic-reflection attributes using autoencoder networks. Geophysics, 85(4), O59-O70.

12. Gao, K., Huang, L., & Zheng, Y. (2021). Fault Detection on Seismic Structural Images Using a Nested Residual U-Net. IEEE Transactions on Geoscience and Remote Sensing.

13. Liu, N., He, T., Tian, Y., Wu, B., Gao, J., & Xu, Z. (2020). Common-azimuth seismic data fault analysis using residual UNet. Interpretation, 8(3), SM25-SM37.

14. Wang, Z., Li, B., Liu, N., Wu, B., & Zhu, X. (2020). Distilling knowledge from an ensemble of convolutional neural networks for seismic fault detection. IEEE Geoscience and Remote Sensing Letters.

15. Wu, X., Geng, Z., Shi, Y., Pham, N., Fomel, S., & Caumon, G. (2020). Building realistic structure models to train convolutional neural networks for seismic structural interpretation. Geophysics, 85(4), WA27-WA39.

16. Wu, X., Liang, L., Shi, Y., Geng, Z., & Fomel, S. (2019). 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, 219(3), 2097-2109.

17. Di, H., Gao, D., & AlRegib, G. (2019). Developing a seismic texture analysis neural network for machine-aided seismic pattern recognition and classification. Geophysical Journal International, 218(2), 1262-1275.

18. Shi, Y., Wu, X., & Fomel, S. (2021). Interactively tracking seismic geobodies with a deep-learning flood-filling network. Geophysics, 86(1), A1-A5.

19. Wrona, T., Pan, I., Bell, R. E., Gawthorpe, R., Fossen, H., & Brune, S. (2020). Deep learning of geological structures in seismic reflecion data.

20. Ao, Y., Lu, W., Jiang, B., & Monkam, P. (2020). Seismic structural curvature volume extraction with convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing.

21. Ao, Y., Lu, W., Xu, P., & Jiang, B. (2021). Seismic Dip Estimation With a Domain Knowledge Constrained Transfer Learning Approach. IEEE Transactions on Geoscience and Remote Sensing.

22. Zheng, Y., Zhang, Q., Yusifov, A., & Shi, Y. (2019). Applications of supervised deep learning for seismic interpretation and inversion. The Leading Edge, 38(7), 526-533.