oncausal spatial prediction filtering based on an ARMA model  

作　　者：Liu Zhipeng Chen Xiaohong Li Jingye 

机构地区：[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

出　　处：《Applied Geophysics》2009年第2期122-128,共7页应用地球物理（英文版）

基　　金：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）.

摘　　要：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.常规频域预测滤波方法是建立在自回归(autoregressive,AR)模型基础上的,这导致滤波过程中前后假设的不一致,即首先利用源噪声的假设计算误差剖面,却又将其作为可加噪声而从原始剖面中减去来得到有效信号。本文通过建立自回归-滑动平均(autoregres sive/moving-average,ARMA)模型,首先求解非因果预测误差滤波算子,然后利用自反褶积形式投影滤波过程估计可加噪声,进而达到去除随机噪声目的。此过程有效避免了基于AR模型产生的不一致性。在此基础上,将一维ARMA模型扩展到二维空间域,实现了基于二维ARMA模型频域非因果空间预测滤波在三维地震资料随机噪声衰减中的应用。模型试验与实际资料处理表明该方法在很好保留反射信息同时,压制随机噪声更加彻底,明显优于常规频域预测去噪方法。

关 键 词：AR model ARMA model noncasual random noise self-deconvolved projection filtering 

分 类 号：TN713[电子电信—电路与系统] O212.1[理学—概率论与数理统计]