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.

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