Noncausal spatial prediction filtering based on an ARMA model
Liu Zhipeng1,2, Chen Xiaohong1,2, and Li Jingye2,3
1. CNPC Key Lab of Geophysical Exploration, China University of Petroleum, Beijing 102249, China.
2. State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Beijing 102249, China.
3. Key Laboratory for Hydrocarbon Accumulation Mechanism, Ministry of Education, China University of Petroleum, Beijing 102249, China.
Abstract Conventional f-x prediction filtering methods are based on an autoregressive model. The error section is first computed as a source noise but is removed as additive noise to obtain the signal, which results in an assumption inconsistency before and after filtering. In this paper, an autoregressive, moving-average model is employed to avoid the model inconsistency. Based on the ARMA model, a noncasual prediction filter is computed and a self-deconvolved projection filter is used for estimating additive noise in order to suppress random noise. The 1-D ARMA model is also extended to the 2-D spatial domain, which is the basis for noncasual spatial prediction filtering for random noise attenuation on 3-D seismic data. Synthetic and field data processing indicate this method can suppress random noise more effectively and preserve the signal simultaneously and does much better than other conventional prediction filtering methods.
This research was financially supported by National Natural Science Foundation of China (Grant No. 40604016) and the National Hi-Tech Research and Development Program (863 Program) (Grants No. 2006AA09A102-09 and No. 2007AA06Z229).
Cite this article:
LIU Zhi-Peng,CHEN Xiao-Hong,LI Jing-Ye. Noncausal spatial prediction filtering based on an ARMA model[J]. APPLIED GEOPHYSICS, 2009, 6(2): 122-128.
[1]
Abma, R., and Claerbout, J., 1995, Lateral prediction for noise attenuation by t-x and f-x techniques: Geophysics, 60(6), 63 - 65.
[2]
Canales, L., 1984, Random noise reduction: 54th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 525 - 527.
[3]
Chase, M., 1992, Random noise reduction by FXY prediction filtering: Exploration Geophysics, 23, 51 - 56.
[4]
Hornbostel, S., 1991, Spatial prediction filtering in the t-x and f-x domains: Geophysics, 56, 2019 - 2026.
[5]
Gulunay, N., 2000, Noncausal spatial prediction filtering for random noise reduction on 3-D poststack data: Geophysics, 65(5), 1641 - 1653.
[6]
Gao, J. H., Mao, J., Man, W. S., Chen, W. C., and Zheng, Q. Q., 2006, On the denoising method of prestack seismic data in wavelet domain: Chinese J. Geophys. (in Chinese), 49(4), 1155 - 1163.
[7]
Guo, J. Y., Zhou, X. Y., and Yang, H. Z., 1995, Attenuation of random noise in (f-x,y) domain: OGP, 30(2), 207 - 215.
[8]
Li, G. F., 1995, Full 3-D random-noise attenuation: OGP, 30(3), 310 - 318.
[9]
Liu, C., Li, H. X., Tao, C. H., Liu, Y., Wang, D., Su, W., and Gu, C. H., 2007, A new fuzzy nesting multilevel median filter and its application to seismic data processing: Chinese J. Geophys. (in Chinese), 50(5), 1534 - 1542.
[10]
Mauricio, D., and Henning, K., 2000, FX ARMA filters: 70th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 2092 - 2095.
[11]
Ozdemir, A., Ozbek, A., Ferber, R., and Zerouk, K., 1999, F-xy projection filtering using helical transformation: 69th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1231 - 1234.
[12]
Soubaras, R., 1994, Signal-preserving random noise attenuation by the f-x projection: 64th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1576 - 1579.
[13]
Soubaras, R., 1995, Deterministic and statistical projection filtering for signal-preserving noise attenuation: 65th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 711 - 714.
[14]
Soubaras, R., 2000, 3D projection filtering for noise attenuation and interpolation: 70th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 2096 - 2099.
[15]
Treitel, S., 1974, The complex Wiener filter: Geophysics, 39, 169 - 173.
[16]
Wang, Y., 1999, Random noise attenuation using forward-backward linear prediction: Journal of Seismic Exploration, 8, 133 - 142.
[17]
Zhong, W., Yang, B. J., and Zhang, Z., 2006, Research on application of polynomial fitting technique in highly noisy seismic data: Progress in Geophysics (in Chinese), 21(1), 184 - 189.
2009, Vol. 6 
