Deep Learning Study on Seismic Data Interpretation Method
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0272-5_22
Reference21 articles.
1. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
2. Gao, K., Huang, L., Zheng, Y.: Fault detection on seismic structural images using a nested residual U-net. IEEE Trans. Geosci. Remote Sens. 60, 4502215 (2022). https://doi.org/10.1109/TGRS.2021.3073840
3. Min, F., Wang, L., Pan, S., Song, G.: D2UNet: dual decoder U-net for seismic image super-resolution reconstruction. IEEE Trans. Geosci. Remote Sens. 61, 5906913 (2023). https://doi.org/10.1109/TGRS.2023.3264459
4. Li, Z., Sun, N., Gao, H., Qin, N., Li, Z.: Adaptive subtraction based on U-net for removing seismic multiples. IEEE Trans. Geosci. Remote Sens. 59(11), 9796–9812 (2021). https://doi.org/10.1109/TGRS.2021.3051303
5. Wang, B., Li, J., Luo, J., Wang, Y., Geng, J.: Intelligent deblending of seismic data based on U-net and transfer learning. IEEE Trans. Geosci. Remote Sens. 59(10), 8885–8894 (2021). https://doi.org/10.1109/TGRS.2020.3048746
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