Abstract
Seismic interpolation is a widely adopted method to improve the resolution of seismic images. During the interpolation of regularly downsampled seismic data, the aliasing problem highly deteriorates the quality of the interpolation results. Nowadays, deep learning has shown great potential in extracting features from data and achieved significant improvement compared with traditional interpolation methods. However, only a few of them have addressed the aliasing problem. In this paper, we propose a novel self-adaptive anti-aliasing framework for seismic data interpolation. We theoretically analyze the aliasing problem in the frequency domain and adopt the shear transform to turn the severely aliased data into less aliased data. Moreover, a closed-loop framework is proposed to automatically evaluate the interpolation results and select the optimal parameter of the shear transform. The experimental results demonstrate that the proposed method can significantly improve the interpolation quality and suppress the aliasing problem.
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