LRSTV: A low-rank total variation-based seismic fault preserving denoising algorithm

Low-rank and total variation techniques have shown their ability in different denoising applications. In this paper, we explore the effectiveness of a combined low-rank and total variation-based denoising algorithm for the first time in seismic data pre-processing. This unified mathematical framework enables restoration of seismic data corrupted by severe degradation. The aforementioned algorithm is capable of suppressing noise without smearing sharp edges. In addition to this, it is also able to sharpen blurred edges in the data. When we apply the algorithm at the pre-processing stage, it highlights edges and discontinuities that often correspond to geological faults and fractures, thereby making structural interpretation much simpler than usual. We evaluate the method through synthetic and field datasets. Results indicate that the method is an effective and robust tool that out-performs other state-of-the art methods for seismic denoising.

Automated summary

In this paper, the effectiveness of a combined low-rank and total variation-based denoising algorithm is explored for the first time in seismic data pre-processing. The algorithm is capable of restoring seismic data corrupted by severe degradation without smearing sharp edges and sharpening blurred edges in the data. The method is evaluated through synthetic and field datasets, and the results indicate its effectiveness and superiority over other state-of-the-art methods for seismic denoising.