Horizon Picking

Horizons are reflection surfaces and an indication of structural boundaries in Earth’s subsurface that are used for mapping reservoir architecture.

Used ML Approaches:

• Artificial Neural Networks

Used Neural Networks:

• FC

• CNN

• U-Net

• Autoencoder

• SegNet

• ResNet

Used Learning Procedures:

• Supervised Learning

• Unsupervised Learning

• Semi-Supervised Learning

References:

1. Liu, X., Xue, P., & Li, Y. (1989). Neural network method for tracing seismic events. In SEG Technical Program Expanded Abstracts 1989 (pp. 716-718). Society of Exploration Geophysicists.

2. Harrigan, E., Kroh, J. R., Sandham, W. A., & Durrani, T. S. (1992, March). Seismic horizon picking using an artificial neural network. In Acoustics, Speech, and Signal Processing, IEEE International Conference on (Vol. 3, pp. 105-108). IEEE Computer Society.

3. Veezhinathan, J., Kemp, F., & Threet, J. (1993, March). A hybrid of neural net and branch and bound techniques for seismic horizon tracking. In Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice (pp. 173-178).

4. Peters, B., Haber, E., & Granek, J. (2019). Neural networks for geophysicists and their application to seismic data interpretation. The Leading Edge, 38(7), 534-540.

5. Peters, B., Granek, J., & Haber, E. (2019). Multiresolution neural networks for tracking seismic horizons from few training images. Interpretation, 7(3), SE201-SE213.

6. Shi, Y., Wu, X., & Fomel, S. (2020). Waveform embedding: Automatic horizon picking with unsupervised deep learning. Geophysics, 85(4), WA67-WA76.

7. Tschannen, V., Delescluse, M., Ettrich, N., & Keuper, J. (2020). Extracting horizon surfaces from 3D seismic data using deep learning. Geophysics, 85(3), N17-N26.

8. Wu, H., Zhang, B., Lin, T., Cao, D., & Lou, Y. (2019). Semiautomated seismic horizon interpretation using the encoder-decoder convolutional neural network. Geophysics, 84(6), B403-B417.

9. Yang, L., & Sun, S. Z. (2020). Seismic horizon tracking using a deep convolutional neural network. Journal of Petroleum Science and Engineering, 187, 106709.

10. Geng, Z., Wu, X., Shi, Y., & Fomel, S. (2020). Deep learning for relative geologic time and seismic horizons. Geophysics, 85(4), WA87-WA100.

11. Di, H., Li, Z., Maniar, H., & Abubakar, A. (2020). Seismic stratigraphy interpretation by deep convolutional neural networks: A semisupervised workflow. Geophysics, 85(4), WA77-WA86.

12. 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.

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