Ground-roll noise attenuation using a simple and effective approach based on local bandlimited orthogonalization |

The simplest and most straightforward way for removing ground-roll noise might be bandpass filtering. Because of the low-frequency property of ground-roll, a simple high-pass filter is often applied to the seismic record to attenuate the ground-roll noise. However, the low-bound-frequency (LBF) for the bandpass filter is not easy to choose, because higher LBF will damage primary reflections while lower LBF will fail to remove enough ground-roll noise. This issue exists because of the frequency overlap of primary reflections and ground-roll noise. One way to solve this problem is to use matched filtering. The low-pass data is used as an initial guess for the ground-roll noise and then a least squares (LS) based matching filter is calculated to match the initial ground-roll noise to the raw seismic data best Yarham et al. (2006); Chiu et al. (2007); Halliday et al. (2010). The matched ground-roll noise is then subtracted from raw seismic data to obtain the output. This adaptive subtraction method depends highly on the initial prediction of the ground-roll noise. Besides, in the case of highly non-stationary primary reflections and ground-roll noise, a conventional stationary matched filtering will fail to obtain an acceptable result.

In this paper, we proposed a simple but effective way for removing all the ground-roll noise without harming useful reflections. We first apply a simple bandpass filter to raw seismic data in order to remove all the ground-roll noise (thus the LBF should be a little bit high to guarantee no ground-roll noise left in the data). Then because of loss of useful primary reflections, we use local signal-and-noise orthogonalization proposed recently by Chen and Fomel (2015) to orthogonalize the primary reflections and ground-roll noise in order to restore the lost useful signals to the initial bandpass filtered data. The local orthogonalization algorithm was initially proposed to compensate for the useful energy loss during a traditional random noise attenuation process. The basic principle of the local orthogonalization methodology is to assume that the useful signal and noise should be orthogonal to each other and then orthogonalize the two components by formulating a regularized inverse problem. We bring the same strategy to this paper to compensate for the energy loss in a simple bandpass filtering based ground-roll noise attenuation approach. The performance of the proposed approach on the pre-stack dataset appears successful and also indicate a broader application of the orthogonalization methodology in removing various types of noise.

Ground-roll noise attenuation using a simple and effective approach based on local bandlimited orthogonalization |

2015-11-24