> 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/data-extrapolation.md).

# Data Extrapolation

It is used to synthesize low-frequency components of the seismic signals based on the recorded high-frequency components or vise versa. 

### **Used ML Approaches:**

* Artificial Neural Networks

### **Used Neural Networks:**

* CNN
* FC
* Autoencoder
* U-Net
* DenseNet

### Used Learning Procedures:

* Supervised Learning

### References:

1. Jia, Z., & Lu, W. (2019). CNN-based ringing effect attenuation of vibroseis data for first-break picking. IEEE Geoscience and Remote Sensing Letters, 16(8), 1319-1323.
2. Ovcharenko, O., Kazei, V., Kalita, M., Peter, D., & Alkhalifah, T. (2019). Deep learning for low-frequency extrapolation from multioffset seismic data. Geophysics, 84(6), R989-R1001.
3. Sun, H., & Demanet, L. (2020). Extrapolated full-waveform inversion with deep learning. Geophysics, 85(3), R275-R288.
4. Fang, J., Zhou, H., Elita Li, Y., Zhang, Q., Wang, L., Sun, P., & Zhang, J. (2020). Data-driven low-frequency signal recovery using deep-learning predictions in full-waveform inversion. Geophysics, 85(6), A37-A43.
5. Li, Y., Song, J., Lu, W., Monkam, P., & Ao, Y. (2020). Multitask learning for super-resolution of seismic velocity model. IEEE Transactions on Geoscience and Remote Sensing.