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 assu...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.展开更多
The distribution of sedimentary microfacies in the eighth member of the Shihezi formation（the H8 member） in the Sul4 3D seismic test area was investigated.A Support Vector Machine（SVM） model was introduced for the...The distribution of sedimentary microfacies in the eighth member of the Shihezi formation（the H8 member） in the Sul4 3D seismic test area was investigated.A Support Vector Machine（SVM） model was introduced for the first time as a way of predicting sandstone thickness in the study area.The model was constructed by analysis and optimization of measured seismic attributes.The distribution of the sedimentary microfacies in the study area was determined from predicted sandstone thickness and an analysis of sedimentary characteristics of the area.The results indicate that sandstone thickness predictions in the study area using an SVM method are good.The distribution of the sedimentary microfacies in the study area has been depicted at a fine scale.展开更多
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）.
文摘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.
基金Financial support for this work,provided by the Major National Science and Technology Special Projects(No.2008ZX05008)
文摘The distribution of sedimentary microfacies in the eighth member of the Shihezi formation（the H8 member） in the Sul4 3D seismic test area was investigated.A Support Vector Machine（SVM） model was introduced for the first time as a way of predicting sandstone thickness in the study area.The model was constructed by analysis and optimization of measured seismic attributes.The distribution of the sedimentary microfacies in the study area was determined from predicted sandstone thickness and an analysis of sedimentary characteristics of the area.The results indicate that sandstone thickness predictions in the study area using an SVM method are good.The distribution of the sedimentary microfacies in the study area has been depicted at a fine scale.
