Abstract
Ground roll could seriously mask the useful reflection signals and decrease the signal-to-noise ratio (S/N) of seismic data, thereby affecting the subsequent seismic data processing. It is challenging for traditional methods to effectively extract high-fidelity reflection signals when ground roll noise and low-frequency reflection signals overlap in the frequency domain. We propose a fully convolutional framework with dense connections to attenuate ground roll (GRDNet) in land seismic data. GRDNet mainly consists of four blocks, which are convolutional, dense, transition down, and transition up blocks. The dense block consists of several convolution blocks to extract the waveform features of the seismic data. The short-long connection in the dense block and the skip connection in the encoder-decoder not only reuses the features extracted by the previous layer but also adds constraints other than the loss function to each convolution block. The well-trained network is tested on one synthetic data and two real land seismic datasets containing strong ground roll with linear and hyperbolic moveouts, respectively. Three traditional and two state-of-the-art deep learning (DL) methods are used as benchmarks to compare denoising performance with GRDNet. The testing results show that the proposed method can effectively attenuate the ground roll in seismic data and preserve useful reflection signals.
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References
Arik SO, Jun H, Diamos G (2019) Fast Spectrogram inversion using multi-head convolutional neural networks. IEEE Signal Process Lett 26:94–98
Biswas R, Sen MK, Das V et al (2019) Prestack and poststack inversion using a physics-guided convolutional neural network. Interpetation 7:SE161–SE174
Chaudhary A (2020) A visual guide to self-labelling images. arXiv preprint arXiv:1502.03167
Chen S, Cao S, Sun Y et al (2022) Seismic time-frequency analysis via time-varying filtering based empirical mode decomposition method. J Appl Geophys 204(104):731
Chen W, Chen Y, Liu W (2016) Ground roll attenuation using improved ccomplete ensemble empirical mode decomposition. J Seism Explorat 25:485–495
Chen W, Yang L, Zha B et al (2020) Deep learning reservoir porosity prediction based on multilayer long short-term memory network. Geophysics 85:WA213–WA225
Chen Y (2021) Nonstationary local time-frequency transform. Geophysics 86:V245–V254
Chen Y, Jiao S, Ma J et al (2015) Ground-Roll noise attenuation using a simple and effective approach based on local band-limited orthogonalization. IEEE Geosci Remote Sens Lett 12:2316–2320
Chen Y, Zhang G, Bai M et al (2019) Automatic waveform classification and arrival picking based on convolutional neural network. Earth Space Sci 6:1244–1261
Colombo D, Turkoglu E, Li W et al (2021) Physics-driven deep-learning inversion with application to transient electromagnetics. Geophysics 86:E209–E224
Deighan AJ, Watts DR (1997) Ground-roll suppression using the wavelet transform. Geophysics 62:1896–1903
Fang J, Zhou H, Li YE et al (2021) Data-driven low-frequency signal recovery using deep-learning predictions in full-waveform inversion. Geophysics 85:A37–A43
Fomel S (2002) Applications of plane-wave destruction filters. Geophysics 67:1946–1960
Fomel S (2013) Seismic data decomposition into spectral components using regularized nonstationary autoregression. Geophysics 78:O69–O76
Foti S, Sambuelli L, Socco L, et al (2002) Spatial sampling issues in FK analysis of surface waves. Presented at the symposium on the application of geophysics to engineering and environmental problems. Las Vegas, Nevada, February 10–14
Gao H, Yuan H, Wang Z et al (2020) Pixel transposed convolutional networks. IEEE Trans Patt Analy Mach Intell 42:1218–1227
Gelisli K, Karsli H (1998) F-K filtering using the Hartley transform. J Seism Explorat 7:101–107
Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. J Mach Learn Res 15:315–323
Halliday D (2011) Adaptive interferometry for ground-roll suppression. Lead Edge 30(5):532–537
He K, Zhang X, Ren S et al (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. IEEE Int Conf Comput Vis 20:1026–1034
Henley DC (2003) Coherent noise attenuation in the radial trace domain. Geophysics 68:1408–1416
Hosseini SA, Javaherian A, Hassani H et al (2015) Adaptive attenuation of aliased ground roll using the shearlet transform. J Appl Geophys 112:190–205
Huang G, Liu Z, Maaten LVD, et al. (2017) Densely connected convolutional networks. In: IEEE conference on computer vision and pattern recognition pp 2261–2269
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167
Ji Y, Kragh E, Bagaini C (2010) Noise attenuation methods for point-receiver land seismic data. 80th SEG Annual International Meeting, Expanded Abstracts, 29:3545–3549
Jia Z, Lu W (2020) Blind separation of ground-roll using interband morphological similarity and pattern coding. IEEE Trans Geosci Remote Sens 58:7166–7177
Jiang X, Liu S, Dai X et al. (2022) Deep metric learning based on meta-mining strategy with semiglobal information. IEEE Trans Neural Networks Learn Syst. https://doi.org/10.1109/TNNLS.2022.3202571
Jiao S, Chen Y, Bai M et al (2015) Ground roll attenuation using non-stationary matching filtering. J Geophys Eng 12:922
Jones IF, Levy S (1987) Signal-to-noise ratio enhancement in multi-channel seismic data via the Karhunen-Loeve transform. Geophys Prospect 35:12–32
de Jonge T, Vinje V, Poole G et al (2022) Debubbling seismic data using a generalized neural network. Geophysics 87(1):V1–V14
Karsli H, Bayrak Y (2004) Using the wiener-levinson algorithm to suppress ground-roll. J Appl Geophys 55:187–197
Kaur H, Fomel S, Pham N (2020) Seismic ground-roll noise attenuation using deep learning. Geophys Prospect 68:2064–2077
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv::14126980
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 69:84–90
Li H, Yang W, Yong X (2018) Deep learning for ground–roll noise attenuation. 75th Annual International Meeting, SEG, Expanded Abstracts pp 1981–1985
Li H, Chen D, Chang D (2019) Ground-roll noise attenuation based on convolutional neural network. In: 76th annual international meeting, SEG, expanded abstracts pp 1981–1985
Li S, Liu B, Ren Y et al (2020) Deep-learning inversion of seismic data. IEEE Trans Geosci Remote Sens 58:2135–2149
Li Y, Wang H, Dong X (2020) The denoising of desert seismic data based on cycle-GAN with unpaired data training. IEEE Geosci Remote Sens Lett 18:2016–2020
Liu G, Chen X, Du J et al (2012) Random noise attenuation using f-x regularized nonstationary autoregression. Geophysics 77(2):V61–V69
Liu X (1999) Ground roll suppression using the Karhunen-Loeve transform. Geophysics 64:564–566
Liu Y, Fomel S (2012) Seismic data analysis using local time-frequency decomposition. Geophys Prospect 61:516–525
Liu Z, Chen Y, Ma J (2018) Ground roll attenuation by synchrosqueezed curvelet transform. J Appl Geophys 151:246–262
Mousavi SM, Ellsworth WL, Zhu W et al (2020) Earthquake transformer-an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature Commun 11:3952
Naghizadeh M, Sacchi M (2018) Ground-roll attenuation using curvelet downscaling. Geophysics 83:V185–V195
Oliveira DAB, Semin DG, Zaytsev S (2020) Self-supervised ground roll noise attenuation using self-labeling and paired data synthesis. IEEE Trans Geosci Remote Sens 59(8):7147–7159
Pham N, Li W (2022) Physics-constrained deep learning for ground roll attenuation. Geophysics 87:V15–V27
Rango R (1989) Suppression of ground roll by windowing in two domains. First Break 7:55–63
Saad OM, Chen Y (2020) Deep denoising autoencoder for seismic random noise attenuation. Geophysics 85:V367–V376
Saatcilar R, Canitez N (1988) A method of ground-roll elimination. Geophysics 53:894–902
Salamon J, Bello JP (2017) Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process Lett 24:279–283
Siahkoohi A, Louboutin M, Herrmann FJ (2019) The importance of transfer learning in seismic modeling and imaging. Geophysics 84:A47–A52
Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958
Strobbia C, Zarkhidze A, May R, et al (2011) Model-based coherent noise attenuation for complex dispersive waves. In: 68th annual international meeting, SEG, expanded abstracts pp 3571–3575
Tenorio L (2001) Modeling non-Gaussian reflectivities: Generalizing Wiener-Levinson deconvolution. Geophysics 66:1913–1920
Verma S, Guo S, Ha T et al (2016) Highly aliased ground-roll suppression using a 3D multiwindow Karhunen-Loeve filter: application to a legacy Mississippi Lime survey. Geophysics 81:V79–V88
Wang C, Huang X, Li KY, Jensen F (2023) Removing multiple types of noise of distributed acoustic sensing seismic data using attention-guided denoising convolutional neural network. Front Earth Sci. https://doi.org/10.3389/feart.2022.986,470
Wang H, Chen W, Huang W et al (2021) Nonstationary predictive filtering for seismic random noise suppression-A tutorial. Geophysics 86(3):W21–W30
Wang W, Gao J, Chen W et al (2012) Data adaptive ground-roll attenuation via sparsity promotion. J Appl Geophys 83:19–28
Wu H, Zhang B, Li F et al (2019) Semiautomatic first-arrival picking of microseismic events by using the pixel-wise convolutional image segmentation method. Geophysics 84:V143–V155
Wu X, Geng Z, Shi Y et al (2020) Building realistic structure models to train convolutional neural networks for seismic structural interpretation. Geophysics 85(4):WA27–WA39
Yang L, Chen W, Wang H et al (2021) Deep learning seismic random noise attenuation via improved residual convolutional neural network. IEEE Trans Geosci Remote Sens 59:7968–7981
Yang L, Wang S, Wang S et al (2021) Unsupervised 3-D random noise attenuation using deep skip autoencoder. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2021.3100,455
Yuan Y, Si X, Zheng Y (2020) Ground-roll attenuation using generative adversarial networks. Geophysics 85:255–267
Zheng J, Yin X, Zhang G et al (2010) The surface wave suppression using the second generation curvelet transform. Appl Geophys 7:325–335
Zhou Y, Yang J, Wang H et al (2021) Statistics-guided dictionary learning for automatic coherent noise suppression. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2020.3039,738
Acknowledgements
This work was supported in part by the National Key R &D Program of China under Grant 2019YFC0312003, in part by the Strategic Cooperation Technology Projects of CNPC and CUPB under Grant ZLZX2020-03, and in part by the R &D Department of China National Petroleum Corporation (Investigations on fundamental experiments and advanced theoretical methods in geophysical prospecting applications) under Grant 2022DQ0604-04.
Funding
This work was supported in part by the National Key R &D Program of China under Grant 2019YFC0312003, in part by the Strategic Cooperation Technology Projects of CNPC and CUPB under Grant ZLZX2020-03, and in part by the R &D Department of China National Petroleum Corporation (Investigations on fundamental experiments and advanced theoretical methods in geophysical prospecting applications) under Grant 2022DQ0604-04.
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Yang, L., Wang, S., Chen, X. et al. Deep Learning with Fully Convolutional and Dense Connection Framework for Ground Roll Attenuation. Surv Geophys 44, 1919–1952 (2023). https://doi.org/10.1007/s10712-023-09779-8
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DOI: https://doi.org/10.1007/s10712-023-09779-8