self-supervised-3-DPCNN
W. Cao, Y. Shi, W. Wang, X. Guo, F. Tian and Y. Zhao, "Self-Supervised Multitask 3-D Partial Convolutional Neural Network for Random Noise Attenuation and Reconstruction in 3-D Seismic Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-19, 2022, Art no. 5924619, doi: 10.1109/TGRS.2022.3225923.
Our code is based on the code corresponding to the article: (1) Y. Quan, M. Chen, T. Pang, et al. "Self2self with dropout: Learning self-supervised denoising from single image," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 1890–1898.
We use the following algorithms as the benchmark algorithms: (2) S. Yu, J. Ma, X. Zhang, et al. "Interpolation and denoising of high-dimensional seismic data by learning a tight frame," Geophysics, vol. 80, no. 5, pp. V119–V132, 2015. (3) Y. Chen, W. Huang, D. Zhang, et al. "An open-source matlab code package for improved rank-reduction 3d seismic data denoising and reconstruction," Computers & Geosciences, vol. 95, pp. 59–66, 2016. (4) O. M. Saad, Y. A. S. I. Oboué, M. Bai, et al. "Self-attention deep image prior network for unsupervised 3-d seismic data enhancement," IEEE Transactions on Geoscience and Remote Sensing, 2021. (5) F. Kong, F. Picetti, V. Lipari, et al. "Deep prior-based unsupervised reconstruction of irregularly sampled seismic data," IEEE Geoscience and Remote Sensing Letters, 2020.
Data from the articles: (6) O. M. Saad, Y. A. S. I. Oboué, M. Bai, et al. "Self-attention deep image prior network for unsupervised 3-d seismic data enhancement," IEEE Transactions on Geoscience and Remote Sensing, 2021. (7) L. Yang, S. Wang, X. Chen, et al. "Unsupervised 3-d random noise attenuation using deep skip autoencoder," IEEE Transactions on Geoscience and Remote Sensing, 2021. and https://wiki.seg.org/wiki/Open_data
We thank the above scholars for opening their codes！