Inversion-based data-driven time-space domain random noise attenuation method
Zhao Yu-Min1,2, Li Guo-Fa1,2, Wang Wei1,2, Zhou Zhen-Xiao3, Tang Bo-Wen3, and Zhang Wen-Bo3
1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China.
2. CNPC Key Laboratory of Geophysical Prospecting, China University of Petroleum, Beijing 102249, China.
3. BGP Geophysical Research Center, Zhuozhou 072750, China.
Abstract：
Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter (PEF), is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data’s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.
Abma, R., and Claerbout, J. F., 1995, Lateral prediction for noise attenuation by t-x and f-x techniques: Geophysics, 60(6), 1887-1896.
[2]
Canales, L. L., 1984, Random noise reduction: 54th Annual International Meeting, SEG, Expanded Abstracts, 525-527.
[3]
Chase, M. K., 1992, Random noise reduction by FXY prediction filtering: Exploration Geophysics, 23(2), 51-56.
[4]
Chen, K., and Sacchi, M. D., 2013, Robust reduced-rank seismic denoising: 83th Annual International Meeting, SEG, Expanded Abstracts, 4272-4277.
[5]
Claerbout, J. F., 1992, Earth soundings analysis: Processing versus inversion: Blackwell Scientific Publications, United Kingdom.
[6]
Crawley, S., Claerbout, J. F., and Clapp, R., 1999, Interpolation with smoothly nonstationary prediction-error filters: 69th Annual International Meeting, SEG, Expanded Abstracts, 1154-1157.
[7]
Freire, Sergio, L. M., and Ulrych, T. J., 1988, Application of singular value decomposition to vertical seismic profiling: Geophysics, 53(6), 778-785.
[8]
Futterman, W. I., 1962, Dispersive body waves: Journal of Geophysical Research, 67(13), 5279-5291.
[9]
Gulunay, N., 1986, FXDECON and complex Wiener prediction filter: 56th Annual International Meeting, SEG, Expanded Abstracts, 279-281.
[10]
Hornbostel, S., 1991, Spatial prediction filtering in the t-x and f-x domains: Geophysics, 56(12), 2019-2026.
[11]
Li, G. F., 1995, Full 3-D random-noise attenuation: Oil Geophysical Prospecting (in Chinese), 30(3), 310-318.
[12]
Li, G. F., Liu, Y., Zheng, H., and Huang, W., 2015, Absorption decomposition and compensation via a two-step scheme: Geophysics, 80(6), 145-155.
[13]
Li, G. F., Sacchi, M. D., and Zheng, H., 2016a, In situ evidence for frequency dependence of near-surface Q: Geophysical Journal International, 204(2), 1308-1315.
[14]
Li, G. F., Zheng, H., Zhu, W. L., et al., 2016b, Tomographic inversion of near-surface Q factor by combining surface and cross-hole seismic surveys: Applied Geophysics, 13(1), 93-102.
[15]
Liu, Y., Liu, N., and Liu, C., 2015, Adaptive prediction filtering in t-x-y domain for random noise attenuation using regularized nonstationary autogression: Geophysics, 80(1), V13-V21.
[16]
Liu, Z., Chen, X., and Li, J., 2009, Noncausal spatial prediction filtering based on an ARMA model: Applied Geophysics, 6(2), 122-128.
[17]
Naghizadeh, M., and Sacchi, M. D., 2009, f-x adaptive seismic-trace interpolation: Geophysics, 74(1), V9-V16.
[18]
Oropeza, V., and Sacchi, M. D., 2011, Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis: Geophysics, 76(3), V25-V32.
[19]
Sacchi, M. D., 2009, FX singular spectrum analysis: Cspg Cseg Cwls Convention.
[20]
Sacchi, M. D., and Kuehl, H., 2001, ARMA formulation of FX prediction error filters and projection filters: Journal of Seismic Exploration, 9(3), 185-197.
[21]
Soubaras, R., 1994, Signal-preserving random noise attenuation by the f-x projection: 64th Annual International Meeting, SEG, Expanded Abstracts, 1576-579.
[22]
Soubaras, R., 1995, Deterministic and statistical projection filtering for signal-preserving noise attenuation: 57th EAGE Annual Meeting, Expanded Abstracts, A051.
[23]
Soubaras, R., 2000, 3D projection filtering for noise attenuation and interpolation: 64th Annual International Meeting, SEG, Expanded Abstracts, 2096-2099.
2017, Vol. 14 
