> 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-trace-interpolation.md).

# Seismic Trace Interpolation

 It is a technique used to reconstruct the missing traces due to the due to existence of physical obstacles, economic constraints, or broken instruments.

### **Used ML Approaches:**

* Artificial Neural Networks

### **Used Neural Networks:**

* CNN
* FC
* U-Net
* Autoencoder
* ResNe
* GAN

### Used Learning Procedures:

* Unsupervised Learning
* Supervised Learning
* Transfer Learning

### References:

1. Mandelli, S., Lipari, V., Bestagini, P., & Tubaro, S. (2019). Interpolation and denoising of seismic data using convolutional neural networks. arXiv preprint arXiv:1901.07927.
2. Wang, B., Zhang, N., Lu, W., & Wang, J. (2019). Deep-learning-based seismic data interpolation: A preliminary result. Geophysics, 84(1), V11-V20.
3. Wang, Y., Wang, B., Tu, N., & Geng, J. (2020). Seismic trace interpolation for irregularly spatial sampled data using convolutional autoencoder. Geophysics, 85(2), V119-V130.
4. Wang, B., Zhang, N., Lu, W., Geng, J., & Huang, X. (2019). Intelligent missing shots’ reconstruction using the spatial reciprocity of Green’s function based on deep learning. IEEE Transactions on Geoscience and Remote Sensing, 58(3), 1587-1597.
5. Tang, S., Ding, Y., Zhou, H. W., & Zhou, H. (2020). Reconstruction of sparsely sampled seismic data via residual U-Net. IEEE Geoscience and Remote Sensing Letters.
6. Huang, J., & Nowack, R. L. (2020). Machine learning using U-net convolutional neural networks for the imaging of sparse seismic data. Pure and Applied Geophysics, 1-16.
7. Chang, D., Yang, W., Yong, X., Zhang, G., Wang, W., Li, H., & Wang, Y. (2020). Seismic data interpolation using dual-domain conditional generative adversarial networks. IEEE Geoscience and Remote Sensing Letters.
8. Chai, X., Tang, G., Wang, S., Peng, R., Chen, W., & Li, J. (2020). Deep learning for regularly missing data reconstruction. IEEE Transactions on Geoscience and Remote Sensing, 58(6), 4406-4423.
9. Chai, X., Tang, G., Wang, S., Lin, K., & Peng, R. (2020). Deep learning for irregularly and regularly missing 3-D data reconstruction. IEEE Transactions on Geoscience and Remote Sensing.
10. Chai, X., Tang, G., Wang, S., Lin, K., & Peng, R. (2020). Deep learning for irregularly and regularly missing 3-D data reconstruction. IEEE Transactions on Geoscience and Remote Sensing.
11. Zhang, H., Yang, X., & Ma, J. (2020). Can learning from natural image denoising be used for seismic data interpolation?. Geophysics, 85(4), WA115-WA136.
12. Kaur, H., Pham, N., & Fomel, S. (2021). Seismic data interpolation using deep learning with generative adversarial networks. Geophysical Prospecting, 69(2), 307-326.
13. Pan, S., Chen, K., Chen, J., Qin, Z., Cui, Q., & Li, J. (2020). A partial convolution-based deep-learning network for seismic data regularization1. Computers & Geosciences, 145, 104609.