Depth-Wise Classification and Geological Characterization of Seismic Salt Bodies Using Quantitative Seismic Attributes

Authors

  • P. Sheela Jasmine ,Dr. V. Joseph Peter

Keywords:

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Abstract

Accurate geological characterization of subsurface salt bodies is essential for hydrocarbonexploration, reservoir evaluation, and seismic interpretation. While recent studies havedemonstrated significant improvements in salt identification, the automatic geological characterization of salt bodies remains a relatively underexplored problem

References

[1] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Lecture Notes in Computer Science, vol. 9351, pp. 234–241, 2015.

[2] V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep Convolutional EncoderDecoder Architecture for Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2481–2495, 2017.

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Published

2024-11-27

How to Cite

P. Sheela Jasmine ,Dr. V. Joseph Peter. (2024). Depth-Wise Classification and Geological Characterization of Seismic Salt Bodies Using Quantitative Seismic Attributes . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 7827–7837. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5844

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