A Denoising Method for Seismic Data Based on SVD and Deep Learning
Source abstract
Abstract
When reconstructing seismic data, the traditional singular value decomposition (SVD) denoising method has the challenge of difficult rank selection. Therefore, we propose a seismic data denoising method that combines SVD and deep learning. In this method, seismic data with different signal-to-noise ratios (SNRs) are processed by SVD. Data sets are created from the decomposed right singular vectors and data sets divided into two categories: effective signal and noise. The lightweight MobileNetV2 network was chosen for training because of its quick response speed and great accuracy. We forecasted and categorized the right singular vectors by SVD using the trained MobileNetV2 network. The right singular vector (RSV) corresponding to the noise in the seismic data was removed during reconstruction, but the effective signal was kept. The effective signal was projected to smooth the RSV. Finally, the goal of low SNR denoising of two-dimensional seismic data was accomplished. This approach addresses issues with deep learning in seismic data processing, including the challenge of gathering sample data and the weak generalizability of the training model. Compared with the traditional denoising method, the improved denoising method performs well at removing Gaussian and irregular noise with strong amplitudes.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Seismic Imaging and Inversion Techniques · Seismic Waves and Analysis · Image and Signal Denoising Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Funding
National Natural Science Foundation of China · Guizhou Science and Technology Cooperation Platform Talents Program
References (28)
Show source-provided references
1. A Branch Construction-Based CNN Denoiser for Desert Seismic Data10.1109/LGRS.2020.2981965 ↗
2. Liu, D., Wang, W., Chen, W., Wang, X., Zhou, Y., and Shi, Z. (2018). Random-Noise Suppression in Seismic Data: What Can Deep Learning Do?. SEG Technical Program Expanded Abstracts 2018, Society of Exploration Geophysicists.10.1190/segam2018-2998114.1 ↗
3. Seismic Signal Denoising Using f-x Variational Mode Decomposition10.1109/LGRS.2019.2948631 ↗
4. Random noise attenuation using f-x regularized nonstationary autoregression10.1190/geo2011-0117.1 ↗
5. Research on Deep Convolutional Neural Network Time-Frequency Domain Seismic Signal Denoising Combined with Residual Dense Blocks10.3389/feart.2021.681869 ↗
6. Streaming orthogonal prediction filter in the t-x domain for random noise attenuation10.1190/geo2017-0322.1 ↗
7. Improved S-Transform and Its Application in Seismic Signal Processing
8. Wavelet transform based on GCV criterion and CUDA technology for ground roll attenuation
9. Simultaneous sources separation via multidirectional vector-median filtering10.1190/geo2011-0254.1 ↗
10. Yang, Y., Lu, J., and Wang, Y. (2018). Vertical Seismic Profile Wavefield Separation Using Median Filtering Constrained by the Linear Radon Transform. Appl. Sci., 8.10.3390/app8091494 ↗
11. Li, B., Huang, H., Wang, T., Wang, M., and Wang, P. (2020, January 14–16). Research on Seismic Signal Classification and Recognition Based on EEMD and CNN. Proceedings of the 2020 IEEE 3rd International Conference on Electronics and Communication Engineering (ICECE), Xi’An, China.10.1109/ICECE51594.2020.9353037 ↗
12. Robust singular value decomposition filtering for low signal-to-noise ratio seismic data10.1190/geo2020-0169.1 ↗
13. Local singular value decomposition for signal enhancement of seismic data10.1190/1.2435967 ↗
14. Robust reduced-rank filtering for erratic seismic noise attenuation10.1190/geo2014-0116.1 ↗
15. Brankovic, M., Gildin, E., Gibson, R.L., and Everett, M.E. (2021). A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm. Appl. Sci., 11.10.3390/app11114874 ↗
16. Denoising Deep Learning Network Based on Singular Spectrum Analysis—DAS Seismic Data Denoising with Multichannel SVDDCNN
17. Spurious signals attenuation using SVD-based Wiener filter for near-surface ambient noise surface wave imaging10.1016/j.jappgeo.2020.104220 ↗
18. A Patch Based Denoising Method Using Deep Convolutional Neural Network for Seismic Image10.1109/ACCESS.2019.2949774 ↗
19. FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation10.1190/geo2018-0646.1 ↗
20. A Convolutional Autoencoder Method for Simultaneous Seismic Data Reconstruction and Denoising
21. SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network10.1190/INT-2018-0235.1 ↗
22. Yin, X., Liu, F., Cai, R., Yang, X., Zhang, X., Ning, M., and Shen, S. (2022). Research on Seismic Signal Analysis Based on Machine Learning. Appl. Sci., 12.10.3390/app12168389 ↗
23. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18–23). MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of the Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
24. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
25. Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv.
26. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
27. Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely connected convolutional networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
28. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv.