Seismic data edge preserving smoothing method based on structural features

Abstract

The present invention relates to the technical field of seismic data processing, and in particular to a seismic data edge preserving smoothing method based on structural features, which performs targeted sub-region de-noising treatment based on the structural information of seismic data and the similarity information of the pre-filtering process. In addition to the random noise removal, the method effectively protects the boundary information of seismic data. The edge preserving smoothing method includes steps of smoothing the seismic data, decomposing the matrix to obtain the structure tensor of the seismic data, and completing the structural feature analysis of the seismic data; using the conventional de-noising method to filter the seismic data and obtaining the local similarity coefficient; building a guide filtering equation; and performing multiple de-noising iterations and other steps.

Description

technical field [0001] The invention relates to the technical field of seismic data processing, in particular to a method for edge-preserving and noise-removing seismic data based on structural features. Background technique [0002] During the acquisition of seismic data, various random disturbances from the outside world will reduce the signal-to-noise ratio of seismic records. Random noise is a kind of interference wave with no apparent velocity and frequency, which will reduce the signal-to-noise ratio of seismic data and directly affect the quality of seismic data processing. In order to improve the signal-to-noise ratio of seismic data, random noise in seismic records needs to be removed. However, the inventors found that traditional denoising methods (such as mean filtering, multi-channel coherent filtering, etc.) will blur discontinuous structural features such as faults, fracture zones, and geological body boundaries, that is, while de-interference eliminates some ...

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Problems solved by technology

However, the inventors found that traditional denoising methods (such as mean filtering, multi-channel coherent filtering, etc.) will blur discontinuous structural features such as faults, fracture zones, and geological body boundaries, that is, while de-interference eliminates some important earthquakes data

This will damage the authenticity of seismic data and is not conducive to improving the fidelity of seismic data

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