> 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/salt-body-detection.md).

# Salt Body Detection

Identifying subsurface salt bodies is indispensable for hydrocarbon exploration and drilling-path planning.

### **Used ML Approaches:**

* Artificial Neural Networks
* Support Vector Machine
* K-Nearest Neighbors

### **Used Neural Networks:**

* RNN
* CNN
* FC
* AlexNet
* U-Net
* ResNet
* DenseNet

### Used Learning Procedures:

* Supervised Learning
* Semi-Supervised Learning
* Ensemble Learning

### References:

1. Huang, K. Y., Liu, W. H., & Chang, I. C. (1989). Hopfield model of neural networks for detection of bright spots. In SEG Technical Program Expanded Abstracts 1989 (pp. 444-446). Society of Exploration Geophysicists.
2. Waldeland, A. U., Jensen, A. C., Gelius, L. J., & Solberg, A. H. S. (2018). Convolutional neural networks for automated seismic interpretation. The Leading Edge, 37(7), 529-537.
3. Babakhin, Y., Sanakoyeu, A., & Kitamura, H. (2019, September). Semi-supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks. In German Conference on Pattern Recognition (pp. 218-231). Springer, Cham.
4. Shi, Y., Wu, X., & Fomel, S. (2019). SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network. Interpretation, 7(3), SE113-SE122.
5. Milosavljević, A. (2020). Identification of salt deposits on seismic images using deep learning method for semantic segmentation. ISPRS International Journal of Geo-Information, 9(1), 24.
6. Sen, S., Kainkaryam, S., Ong, C., & Sharma, A. (2020). SaltNet: A production-scale deep learning pipeline for automated salt model building. The Leading Edge, 39(3), 195-203.
7. Rizk, Y., Partamian, H., & Awad, M. (2017). Toward real-time seismic feature analysis for bright spot detection: A distributed approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(1), 322-331.
8. 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.
9. Shi, Y., Wu, X., & Fomel, S. (2021). Interactively tracking seismic geobodies with a deep-learning flood-filling network. Geophysics, 86(1), A1-A5.
10. Wrona, T., Pan, I., Bell, R. E., Gawthorpe, R., Fossen, H., & Brune, S. (2020). Deep learning of geological structures in seismic reflection data.