# Seismic Denoising

The task of removing/suppressing the unwanted energies from recorded seismic data. These unwanted energies (i.e. seismic noise) has often non-stationary characteristics and could be random or non-random overlapping the seismic signal of interest. 

### **Used ML Approaches:**

* Artificial Neural Networks
* Dictionary Learning
* Support Vector Machine

### **Used Neural Networks:**

* Autoencoder
* DnCNN
* U-Net
* ResNet
* GAN
* VGG
* SegNet
* DenseNet
* Attention

### Used Learning Procedures:

* Supervised Learning 
* Unsupervised Learning 
* Transfer Learning 
* Self-Supervised Learning
* Semi-Supervised Learning

### References:

1. Othman, A., Iqbal, N., Hanafy, S. M., & Waheed, U. B. (2021). Automated Event Detection and Denoising Method for Passive Seismic Data Using Residual Deep Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing.
2. Buffenmyer, V., Poulton, M., & Johnson, R. (2000). Identification of seismic crew noise in marine surveys by neural networks. The Leading Edge, 19(4), 370-376.
3. Kimiaefar, R., Siahkoohi, H. R., Hajian, A. R., & Kalhor, A. (2016). Seismic random noise attenuation using artificial neural network and wavelet packet analysis. Arabian Journal of Geosciences, 9(3), 234.
4. Chen, Y., Zhang, M., Bai, M., & Chen, W. (2019). Improving the signal‐to‐noise ratio of seismological datasets by unsupervised machine learning. Seismological Research Letters, 90(4), 1552-1564.
5. Dong, X. T., Li, Y., & Yang, B. J. (2019). Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic. Geophysical Journal International, 219(2), 1281-1299.
6. Mandelli, S., Lipari, V., Bestagini, P., & Tubaro, S. (2019). Interpolation and denoising of seismic data using convolutional neural networks. arXiv preprint arXiv:1901.07927.
7. Richardson, A., & Feller, C. (2019). Seismic data denoising and deblending using deep learning. arXiv preprint arXiv:1907.01497.
8. Si, X., Yuan, Y., Si, T., & Gao, S. (2019). Attenuation of random noise using denoising convolutional neural networks. Interpretation, 7(3), SE269-SE280.
9. Wang, F., & Chen, S. (2019). Residual learning of deep convolutional neural network for seismic random noise attenuation. IEEE Geoscience and Remote Sensing Letters, 16(8), 1314-1318.
10. Wang, E., & Nealon, J. (2019). Applying machine learning to 3D seismic image denoising and enhancement. Interpretation, 7(3), SE131-SE139.
11. Wu, H., Zhang, B., Lin, T., Li, F., & Liu, N. (2019). White noise attenuation of seismic trace by integrating variational mode decomposition with convolutional neural network. Geophysics, 84(5), V307-V317.
12. Yu, S., Ma, J., & Wang, W. (2019). Deep learning for denoising. Geophysics, 84(6), V333-V350.
13. Zhang, M., Liu, Y., & Chen, Y. (2019). Unsupervised seismic random noise attenuation based on deep convolutional neural network. IEEE Access, 7, 179810-179822.
14. Zhang, Y., Lin, H., Li, Y., & Ma, H. (2019). A patch based denoising method using deep convolutional neural network for seismic image. IEEE Access, 7, 156883-156894.
15. Zhang, M., Liu, Y., Bai, M., & Chen, Y. (2019). Seismic noise attenuation using unsupervised sparse feature learning. IEEE Transactions on Geoscience and Remote Sensing, 57(12), 9709-9723.
16. Zhu, W., Mousavi, S. M., & Beroza, G. C. (2019). Seismic signal denoising and decomposition using deep neural networks. IEEE Transactions on Geoscience and Remote Sensing, 57(11), 9476-9488.
17. Liu, D., Deng, Z., Wang, C., Wang, X., & Chen, W. (2020). An Unsupervised Deep Learning Method for Denoising Prestack Random Noise. IEEE Geoscience and Remote Sensing Letters.
18. Liu, D., Wang, W., Wang, X., Wang, C., Pei, J., & Chen, W. (2019). Poststack seismic data denoising based on 3-D convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 58(3), 1598-1629.
19. Saad, O. M., & Chen, Y. (2020). Deep denoising autoencoder for seismic random noise attenuation. Geophysics, 85(4), V367-V376.
20. Viens, L., & Van Houtte, C. (2020). Denoising ambient seismic field correlation functions with convolutional autoencoders. Geophysical Journal International, 220(3), 1521-1535.
21. Wang, S., Li, Y., Wu, N., Zhao, Y., & Yao, H. (2020). Attribute-Based Double Constraint Denoising Network for Seismic Data. IEEE Transactions on Geoscience and Remote Sensing, 59(6), 5304-5316.
22. Yang, L., Chen, W., Liu, W., Zha, B., & Zhu, L. (2020). Random noise attenuation based on residual convolutional neural network in seismic datasets. Ieee Access, 8, 30271-30286.
23. Gao, Y., Zhao, P., Li, G., & Li, H. (2021). Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach. Geophysical Prospecting, 69(5), 984-1002.
24. Li, J., Wu, X., & Hu, Z. (2021). Deep learning for simultaneous seismic image super-resolution and denoising. IEEE Transactions on Geoscience and Remote Sensing.
25. Qiu, C., Wu, B., Liu, N., Zhu, X., & Ren, H. (2021). Deep learning prior model for unsupervised seismic data random noise attenuation. IEEE Geoscience and Remote Sensing Letters.
26. Saad, O. M., & Chen, Y. (2021). A fully unsupervised and highly generalized deep learning approach for random noise suppression. Geophysical Prospecting, 69(4), 709-726.
27. Sang, W., Yuan, S., Yong, X., Jiao, X., & Wang, S. (2020). DCNNs-Based Denoising With a Novel Data Generation for Multidimensional Geological Structures Learning. IEEE Geoscience and Remote Sensing Letters.
28. Tian, X., Lu, W., & Li, Y. (2021). Improved Anomalous Amplitude Attenuation Method Based on Deep Neural Networks. IEEE Transactions on Geoscience and Remote Sensing.
29. Yang, L., Chen, W., Wang, H., & Chen, Y. (2021). Deep Learning Seismic Random Noise Attenuation via Improved Residual Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing.
30. Zhao, Y., Li, Y., Dong, X., & Yang, B. (2018). Low-frequency noise suppression method based on improved DnCNN in desert seismic data. IEEE Geoscience and Remote Sensing Letters, 16(5), 811-815.
31. Zhao, X., Lu, P., Zhang, Y., Chen, J., & Li, X. (2019). Swell-noise attenuation: A deep learning approach. The Leading Edge, 38(12), 934-942.
32. Dong, X., Wang, H., Zhong, T., & Li, Y. (2021). Desert mixed seismic noise suppression by using multiple forward models and a supervised deep-learning method. Exploration Geophysics, 52(4), 431-445.
33. Dong, X., Zhong, T., & Li, Y. (2020). New suppression technology for low-frequency noise in desert region: The improved robust principal component analysis based on prediction of neural network. IEEE Transactions on Geoscience and Remote Sensing, 58(7), 4680-4690.
34. Kaur, H., Fomel, S., & Pham, N. (2020). Seismic ground‐roll noise attenuation using deep learning. Geophysical Prospecting, 68(7), 2064-2077.
35. Li, Y., Wang, H., & Dong, X. (2020). The denoising of desert seismic data based on cycle-GAN with unpaired data training. IEEE Geoscience and Remote Sensing Letters.
36. Ma, H., Yao, H., Li, Y., & Wang, H. (2019). Deep residual Encoder–Decoder networks for desert seismic noise suppression. IEEE Geoscience and Remote Sensing Letters, 17(3), 529-533.
37. Oliveira, D. A., Semin, D. G., & Zaytsev, S. (2020). Self-supervised ground-roll noise attenuation using self-labeling and paired data synthesis. IEEE Transactions on Geoscience and Remote Sensing.
38. Li, Y., Zhao, Z., Tian, Y., & Feng, Q. (2021). Multi-scale DCFF network: a new desert low-frequency noise suppression method. Acta Geodaetica et Geophysica, 56(2), 357-371.
39. You, J., Xue, Y., Cao, J., & Li, C. (2020). Attenuation of seismic swell noise using convolutional neural networks in frequency domain and transfer learning. Interpretation, 8(4), T941-T952.
40. Yuan, Y., Si, X., & Zheng, Y. (2020). Ground-roll attenuation using generative adversarial networks. Geophysics, 85(4), WA255-WA267.
41. Zhao, Y. X., Li, Y., & Yang, B. J. (2020). Denoising of seismic data in desert environment based on a variational mode decomposition and a convolutional neural network. Geophysical Journal International, 221(2), 1211-1225.
42. Zhao, Y., Li, Y., & Yang, B. (2019). Low-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 58(1), 650-665.
43. Li, Y., Luo, X., Wu, N., & Dong, X. (2021). The Application of Semisupervised Attentional Generative Adversarial Networks in Desert Seismic Data Denoising. IEEE Geoscience and Remote Sensing Letters.
44. Zhang, Y., Lin, H., Li, Y., Ma, H., & Yao, G. (2021). Low-Frequency Seismic Noise Reduction Based on Deep Complex Reaction-Diffusion Model. IEEE Transactions on Geoscience and Remote Sensing.
45. Beckouche, S., & Ma, J. (2014). Simultaneous dictionary learning and denoising for seismic data. Geophysics, 79(3), A27-A31.
46. Zhu, L., Liu, E., & McClellan, J. H. (2015). Seismic data denoising through multiscale and sparsity-promoting dictionary learning. Geophysics, 80(6), WD45-WD57.
47. Chen, Y. (2017). Fast dictionary learning for noise attenuation of multidimensional seismic data. Geophysical Journal International, 209(1), 21-31.
48. Zhang, C., van der Baan, M., & Chen, T. (2019). Unsupervised dictionary learning for signal‐to‐noise ratio enhancement of array data. Seismological Research Letters, 90(2A), 573-580.
49. Wang, X., & Ma, J. (2019). Adaptive dictionary learning for blind seismic data denoising. IEEE Geoscience and Remote Sensing Letters, 17(7), 1273-1277.
50. Chen, Y., Ma, J., & Fomel, S. (2016). Double-sparsity dictionary for seismic noise attenuation. Geophysics, 81(2), V103-V116.
51. Lv, H. (2019). SPARSE DICTIONARY LEARNING FOR NOISE ATTENUATION IN THE EXACTLY FLATTENED DIMENSION. JOURNAL OF SEISMIC EXPLORATION, 28(5), 449-474.
52. Zu, S., Zhou, H., Wu, R., Jiang, M., & Chen, Y. (2019). Dictionary learning based on dip patch selection training for random noise attenuation. Geophysics, 84(3), V169-V183.
53. Zhu, L., Liu, E., & McClellan, J. H. (2017). Joint seismic data denoising and interpolation with double-sparsity dictionary learning. Journal of Geophysics and Engineering, 14(4), 802-810.
54. Wu, J., & Bai, M. (2018). Attenuating seismic noise via incoherent dictionary learning. Journal of Geophysics and Engineering, 15(4), 1327.
55. Zhou, Y., Li, S., Xie, J., Zhang, D., & Chen, Y. (2017). Sparse dictionary learning for seismic noise attenuation using a fast orthogonal matching pursuit algorithm. Journal of Seismic Exploration, 26(5), 433-454.
56. Liu, L., Ma, J., & Plonka, G. (2018). Sparse graph-regularized dictionary learning for suppressing random seismic noise. Geophysics, 83(3), V215-V231.
57. Liu, L., & Ma, J. (2018). Structured graph dictionary learning and application on the seismic denoising. IEEE Transactions on Geoscience and Remote Sensing, 57(4), 1883-1893.
58. Nazari Siahsar, M. A., Gholtashi, S., Kahoo, A. R., Chen, W., & Chen, Y. (2017). Data-driven multitask sparse dictionary learning for noise attenuation of 3D seismic data. Geophysics, 82(6), V385-V396.
59. Turquais, P., Asgedom, E. G., & Söllner, W. (2017). Coherent noise suppression by learning and analyzing the morphology of the data. Geophysics, 82(6), V397-V411.
60. Li, C., Zhang, Y., & Mosher, C. C. (2019). A hybrid learning-based framework for seismic denoising. The Leading Edge, 38(7), 542-549.
61. Dong, X., & Li, Y. (2020). Denoising the optical fiber seismic data by using convolutional adversarial network based on loss balance. IEEE Transactions on Geoscience and Remote Sensing.
62. Zhao, Y., Li, Y., & Wu, N. (2020). Distributed Acoustic Sensing Vertical Seismic Profile Data Denoiser Based on Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing.
63. Feng, Q., & Li, Y. (2021). Denoising Deep Learning Network Based on Singular Spectrum Analysis--DAS Seismic Data Denoising With Multichannel SVDDCNN. IEEE Transactions on Geoscience and Remote Sensing.
64. van den Ende, M., Lior, I., Ampuero, J. P., Sladen, A., Ferrari, A., & Richard, C. (2021). A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing data.
65. Baardman, R. H., & Hegge, R. F. (2020). Machine learning approaches for use in deblending. The Leading Edge, 39(3), 188-194.
66. Sun, J., Slang, S., Elboth, T., Larsen Greiner, T., McDonald, S., & Gelius, L. J. (2020). A convolutional neural network approach to deblending seismic data. Geophysics, 85(4), WA13-WA26.
67. Zu, S., Cao, J., Qu, S., & Chen, Y. (2020). Iterative deblending for simultaneous source data using the deep neural network. Geophysics, 85(2), V131-V141.
68. Wang, B., Li, J., Luo, J., Wang, Y., & Geng, J. (2021). Intelligent Deblending of Seismic Data Based on U-Net and Transfer Learning. IEEE Transactions on Geoscience and Remote Sensing.
69. Calderón-Macías, C., Sen, M. K., & Stoffa, P. L. (1997). Hopfield neural networks, and mean field annealing for seismic deconvolution and multiple attenuation. Geophysics, 62(3), 992-1002.
70. Essenreiter, R., Karrenbach, M., & Treitel, S. (1998). Multiple reflection attenuation in seismic data using backpropagation. IEEE transactions on signal processing, 46(7), 2001-2011.
71. Li, Z., & Gao, H. (2020). Feature extraction based on the convolutional neural network for adaptive multiple subtraction. Marine Geophysical Research, 41(2), 1-20.
72. Li, Z., Sun, N., Gao, H., Qin, N., & Li, Z. (2021). Adaptive Subtraction Based on U-Net for Removing Seismic Multiples. IEEE Transactions on Geoscience and Remote Sensing.
73. Li, Z. X. (2020). Adaptive multiple subtraction based on support vector regression. Geophysics, 85(1), V57-V69.
74. Sacramento, I., Trindade, E., Roisenberg, M., Bordignon, F., & Rodrigues, B. B. (2018). Acoustic impedance deblurring with a deep convolution neural network. IEEE geoscience and remote sensing letters, 16(2), 315-319.
75. Ormiston, R., Nguyen, T., Coughlin, M., Adhikari, R. X., & Katsavounidis, E. (2020). Noise reduction in gravitational-wave data via deep learning. Physical Review Research, 2(3), 033066.


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