> For the complete documentation index, see [llms.txt](https://smousavi05.gitbook.io/mlseismology/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://smousavi05.gitbook.io/mlseismology/seismological-tasks/seismic-velocity-picking.md).

# Seismic Velocity Picking

It is used for normal moveout correction and stacking. 

### **Used ML Approaches:**

* Artificial Neural Networks

### **Used Neural Networks:**

* FC
* RNN
* CNN
* VGG
* U-Net

### Used Learning Procedures:

* Unsupervised Learning
* Supervised Learning
* Transfer Learning

### References:

1. Calderón-Mac ı´ as, C., Sen, M. K., & Stoffa, P. L. (1998). Automatic NMO correction and velocity estimation by a feedforward neural network. Geophysics, 63(5), 1696-1707.
2. Biswas, R., Vassiliou, A., Stromberg, R., & Sen, M. K. (2019). Estimating normal moveout velocity using the recurrent neural network. Interpretation, 7(4), T819-T827.
3. Park, M. J., & Sacchi, M. D. (2020). Automatic velocity analysis using convolutional neural network and transfer learning. Geophysics, 85(1), V33-V43.
4. Huang, W. L., Gao, F., Liao, J. P., & Chuai, X. Y. (2021). A deep learning network for estimation of seismic local slopes. Petroleum Science, 18(1), 92-105.
5. Ferreira, R. S., Oliveira, D. A., Semin, D. G., & Zaytsev, S. (2020). Automatic velocity analysis using a hybrid regression approach with convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 59(5), 4464-4470.
6. Wang, W., McMechan, G. A., Ma, J., & Xie, F. (2021). Automatic velocity picking from semblances with a new deep-learning regression strategy: Comparison with a classification approach. Geophysics, 86(2), U1-U13.