Abstract
Seismic data denoising and interpolation are generally essential steps for reflection processing and imaging workflow especially for the complex surface geologic conditions and the irregular acquisition field area. The rank-reduction method is a valid way for the attenuation of random noise and data interpolation by selecting the suitable threshold, i.e., the rank of the useful signals. However, it is difficult for the traditional rank-reduction method to select an appropriate threshold. In this paper, we propose an adaptive rank-reduction method based on the energy entropy to automatically estimate the rank as the threshold for seismic data processing and interpolation. This method considers the energy entropy into the traditional rank-reduction method. The energy entropy of signals can be used to indicate the energy intensity of a signal component in the total energy. The difference of the energy entropy between the useful signals and random noise is perceived as a measurement for selecting the appropriate threshold. Synthetic and field examples indicate that the proposed method can well achieve the attenuation of random noise and interpolation automatically without the estimation of the ranks and demonstrate the feasibility of the new adaptive method in seismic data denoising and interpolation.
Highlights
Seismic data denoising and interpolation play a vital role in seismic processing and have an influence on seismic interpretation
The random background noise and the irregular sampled data are commonly present in field data because of geologic conditions and economic factors, which lead to the poor quality of seismic imaging
In order to overcome the problem of rank estimation, we develop an adaptive rank-reduction method based on energy entropy to automatically estimate the rank as the threshold. e energy entropy of signals can be used to indicate the energy intensity of a signal component in the total energy. e difference of the energy entropy between the useful signals and random noise is perceived as a measure for selecting the optimal threshold. e optimal rank is used to suppress the random noise and recover the original signals via the damped MSSA (DMSSA) algorithm. e structure of the paper is organized as follows
Summary
Zilin Lu ,1,2 Nuan Xia, Liang Sun, Wenxing Xu, Guangcheng Zhang, Haiyue Dou, and Qifeng Jiang. E rank-reduction method is a valid way for the attenuation of random noise and data interpolation by selecting the suitable threshold, i.e., the rank of the useful signals. It is difficult for the traditional rank-reduction method to select an appropriate threshold. We propose an adaptive rank-reduction method based on the energy entropy to automatically estimate the rank as the threshold for seismic data processing and interpolation. Synthetic and field examples indicate that the proposed method can well achieve the attenuation of random noise and interpolation automatically without the estimation of the ranks and demonstrate the feasibility of the new adaptive method in seismic data denoising and interpolation
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