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.
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.
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2009, Vol. 6 
