Seismic wavefield information extraction method based on adaptive local singular value decomposition

The key to seismic data processing is to extract the wavefield information related to the subsurface geological targets. In this paper, we first introduce the low-rank signal priority and the strong energy signal priority characteristics of the Singular Value Decomposition (SVD) method. By analyzing the characteristics of the second-order difference spectrum of typical seismic data, we find two indicative parameters to adaptively select the singular value threshold for extracting reflection and diffraction events. Our proposed Adaptive Local Singular Value Decomposition (ALSVD) method takes advantage of the local analysis, and can effectively extract the weak signals in the seismic data. It is a robust method with good resolution and separation integrity, which is beneficial to seismic migration, seismic imaging, and evaluation of Oil and gas traps. Synthetic examples and field examples demonstrate the effectiveness of this method. •The low-rank signal priority characteristics of SVD is introduced.•The strong energy signal priority characteristics of SVD is introduced.•We propose an ALSVD method for extracting reflection and diffraction events.•It's a robust method with good resolution and integrity information.