Structure-oriented Noise Reduction Method for Seismic Images Based on Regularized Hybrid Norm Filtering

Abstract

The invention discloses an earthquake image structure guiding noise reduction method based on regularization mixed norm filtering. The earthquake image structure guiding noise reduction method includes the following steps that a gradient structure tensor is solved for an input three-dimensional earthquake image; regularization mixed norm filtering is conducted on the gradient structure tensor; a diffusion tensor is designed according to the eigenvalue and eigenvector of the filtered gradient structure tensor; continuity factors are calculated, the continuity factors at the position of a boundary fault feather edge and the like are close to zero, and the maintain performance of the structure is achieved; a sobel operator serves as a derivation operator so that divergence can be calculated. By means of the earthquake image structure guiding noise reduction method based on regularization mixed norm filtering, the textured edge information of earthquake-related data can be reserved, Gaussian noise, ultra Gaussian noise and sub Gaussian noise can be effectively suppressed, and therefore an efficient noise reduction method is achieved.

Description

technical field [0001] The invention relates to the field of seismic image processing, in particular to a seismic image structure-guided noise reduction method based on regularized mixed norm filtering. Background technique [0002] In petroleum seismic exploration, in order to obtain more abundant seismic wave reflection information, seismic wave signals are usually received in a broadband manner, which also reduces the signal-to-noise ratio of seismic data. In addition, the reflection of seismic wave signals by complex geological structures is weak, making The target area in the imaging map of seismic data is blurred, unclear, or even submerged in noise and cannot be identified, which brings errors to subsequent attribute analysis, wave impedance inversion, fracture prediction, etc., and greatly affects the application of seismic data for structural interpretation. and lithology identification. Research on the method of separating effective signal and noise of seismic dat...

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Problems solved by technology

[0008] As mentioned above, the interference of seismic waves in seismic data is very complex, resulting in complex noise in seismic data (including sub-Gaussian, Gaussian and super-Gaussian, etc.), but in the existing structure-oriented filtering method, due to the tensor Volume filtering uses Gaussian window filtering, so only Gaussian noise can be filtered out

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