News and Announcements
ARTICLE
Seismic noise attenuation based on a dip-separated filtering method
HUI LV
Show Less
School of Civil Engineering and Architecture, Nanchang Hangkong University, 696 South Fenghe Av., Nanchang 330063, Jiangxi Province, P. R. China.,
JSE 2020, 29(4), 327–342;
Submitted: 23 April 2019 | Accepted: 4 March 2020 | Published: 1 August 2020
© 2020 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract
Lv, H., 2020. Seismic noise attenuation based on a dip-separated filtering method. Journal of Seismic Exploration, 29: 327-342. Mode decomposition and reconstruction is a commonly used denoising algorithm for seismic data. The principle of the decomposition based method is that the signal and noise can be represented by different parts in a mode decomposition process. While the eatures of useful signals can be captured by the principal components, the noise is separated out by rejecting the less important components during the reconstruction process. The decomposition based method can be optimally applied in the requency-space domain, where signal and noise are separated by their differences in the wavenumber spectrum. The useful signals are mainly corresponding to the ow-wavenumber components, i.e., less oscillating, while the noise corresponds to the ighly oscillating components. Such decomposition acts as a dip filter, which can be combined with a spatial coherency based smoothing operator. The overall algorithm is thus a dip-separated structural filtering method. In this paper, we use the variational mode decomposition (VMD) method to decompose the seismic data into several dipping components, which is followed by a low-rank approximation filtering step. We apply the proposed method to both synthetic and field data examples and obtain satisfactory results.
Keywords
noise attenuation
filtering
variational mode decomposition
References
- Chen, Y., Bai, M. and Chen, Y., 2019. Obtaining free array data by multi-dimensionalseismic reconstruction. Nature Comm., 10: 4434.
- Chen, Y. and Fomel, S., 2015. Random noise attenuation using local signal-and-noiseorthogonalization. Geophysics, 80: WD1-WD9.
- Chen, Y. and Ma, J., 2014. Random noise attenuation by f-x empirical modedecomposition predictive filtering. Geophysics, 79: V81-V91.
- Chen, Y., Ma, J. and Fomel, S., 2016. Double-sparsity dictionary for seismic noiseattenuation. Geophysics, 81(2): V17-V30.
- Chen, Y., Zhou, Y., Chen, W., Zu, S., Huang, W. and Zhang, D., 2017. Empirical lowrank decomposition for seismic noise attenuation. IEEE Transact. Geosci. RemoteSens., 55: 4696-4711.
- Dragomiretskiy, K. and Zosso, D., 2014. Variational mode decomposition. IEEETransact. Signal Process., 62: 531-544.
- Gan, S., Wang, S., Chen, Y., Qu, S. and Zu, S., 2016. Velocity analysis ofsimultaneous-source data using high-resolution semblance-coping with the strongnoise. Geophys. J. Internat., 204: 768-779.
- Gulunay, N., 2000. Noncausal spatial prediction filtering for random noise reduction on3-D poststack data. Geophysics, 65: 1641-1653.
- Hestenes, M.R., 1969. Multiplicr and gradient methods. J. Optimiz. Theory Appl., 4:303-320.
- Huang, W., Wang, R., Chen, Y., Li, H. and Gan, S., 2016. Damped multichannelsingular spectrum analysis for 3D random noise attenuation. Geophysics, 81(4):V261-V270.
- Huang, W., Wang, R., Chen, X. and Chen, Y., 2017a. Double least squares projectionsmethod for signal estimation. IEEE Transact. Geosci. Remote Sens., 55: 4111-4129.
- Huang, W., Wang, R., Yuan, Y., Gan, S. and Chen, Y., 2017b. Signal extraction usingrandomized-order multichannel singular spectrum analysis. Geophysics, 82(2):V59-V74.
- Jiao, S., Chen, Y., Bai, M., Yang, W., Wang, E. and Gan, S., 2015. Ground rollsattenuation using non-stationary matching filtering. J. Geophys. Engineer., 12: 922-
- Liu, C., Chen, C., Wang, D., Liu, Y., Wang, S. and Zhang, L., 2015a. Seismic dipestimation based on the two-dimensional Hilbert transform and its application inrandom noise attenuation. Appl. Geophys., 12: 55-63.
- Liu, G. and Chen, X., 2013. Noncausal f-x-y regularized nonstationary predictionfiltering for random noise attenuation on 3D seismic data. J. Appl. Geophys., 93:60-66.
- Liu, G., Chen, X., Du, J. and Wu, K., 2012. Random noise attenuation using f-xregularized nonstationary autoregression: Geophysics, 77: V61-V69.
- Liu, L., Ma, J., Zhang, X. and Plonka, G., 2018a. Sparse graph-regularized dictionarylearning for suppressing random seismic noise. Geophysics, 83(3): V215-V231.
- Liu, W., Cao, S. and Chen, Y., 2016. Applications of variational mode decomposition inseismic time-frequency analysis. Geophysics, 81(5): V365-V378.
- Liu, W., Cao, S., Jin, Z., Wang, Z. and Chen, Y., 2018b, A novel hydrocarbon detectionapproach via high-resolution frequency-dependent AVO inversion based onvariational mode decomposition. IEEE Transact. Geosci. Remote Sens., 56:2007-2024.
- Liu, W., Cao, S., Wang, Z., Kong, X. and Chen, Y., 2017. Spectral decomposition forhydrocarbon detection based on VMD and Teager-Kaiser energy. IEEE Geosci.Remote Sens. Lett., 14: 539-543.
- Liu, Y., Fomel, S. and Liu, C., 2015b. Signal and noise separation in prestack seismicdata using _velocity-dependent selslet transform. Geophysics, 80(6):WD117-WD128.
- Mousavi, S.M. and Langston, C.A., 2017. Automatic noise-removal/signal-removalbased on general cross-validation thresholding in synchrosqueezed domain and itsapplication on earthquake data. Geophysics, 82(4): V211-V227.
- Naghizadeh, M., and Sacchi, M.D., 2012. Multicomponent seismic random noiseattenuation via vector autoregressive operators: Geophysics, 78, no. 2, V91-V99.
- Oropeza, V. and Sacchi, M., 2010, A randomized SVD for multichannel singularspectrum analysis (mssa) noise attenuation. Expanded Abstr., 80th Ann. Internat.SEG Mtg., Denver: 3539-3544.
- Oropeza, V. and Sacchi, M.D., 2011. Simultaneous seismic data denoising andreconstruction via multichannel singular spectrum analysis. Geophysics, 76:V25-V32.
- Siahsar, M.A.N., Gholtashi, S., Olyaei, E., Chen, W. and Chen, Y., 2017. Simultaneousdenoising and interpolation of 3D seismic data via damped data-driven optimalsingular value shrinkage. IEEE Geosci. Remote Sens. Lett., 14: 1086-1090.
- Tian, Y., Li, Y. and Yang, B., 2014. Variable-eccentricity hyperbolic-trace TFPF forseismic random noise attenuation. IEEE Transact. Geosci. Remote Sens., 52:6449-6458.
- Xue, Y., Yang, J., Ma, J. and Chen, Y., 2016. Amplitude-preserving nonlinear adaptivemultiple attenuation using the high-order sparse radon transform. J. Geophys.Engineer., 13: 207-219.
- Yang, W., Wang, R., Chen, Y., Wu, J., Qu, S., Yuan, J. and Gan, S., 2015. Applicationof spectral decomposition using regularized non-stationary autoregression to randomnoise attenuation. J. Geophys. Engineer., 12: 175-187.
- Zhang, D., Zhou, Y., Chen, H., Chen, W., Zu, S. and Chen, Y., 2017. Hybridrank-sparsity constraint model for simultaneous reconstruction and denoising of 3Dseismic data. Geophysics, 82(5): V351-V367.
- Zhou, Y., Li, S., Zhang, D. and Chen, Y., 2018. Seismic noise attenuation using anonline subspace tracking algorithm. Geophys. J. Internat., 212: 1072-1097.
- Zhou, Y. and Zhang, S., 2017. Robust noise attenuation based on nuclear normminimization and a trace prediction strategy. J. Appl. Geophys., 147: 52-67.
- Zu, S., Zhou, H., Chen, Y., Qu, S., Zou, X., Chen, H. and Liu, R., 2016. A periodicallyvarying code for improving deblending of simultaneous sources in marineacquisition. Geophysics, 81: V213-V225.
- Zu, S., Zhou, H., Mao, W., Zhang, D., Li, C., Pan, X. and Chen, Y., 2017. Iterativedeblending of simultaneous-source data using a coherency-pass shaping operator.Geophys. J. Internat., 211: 541-557.
