Seismic irregular noise removing method based on robust singular spectrum analysis

Abstract

The invention relates to a seismic irregular noise removing method based on robust singular spectrum analysis. The seismic irregular noise removing method is characterized by comprising the followingsteps that the single frequency seismic data are embedded in the Hankel matrix so as to construct the Hankel matrix of the single frequency seismic data; the objective function of the Hankel matrix ofthe single frequency seismic data is obtained based on the L1 and L2 mixed norm; the objective function is solved according to the preset regularization parameters in the objective function so as toobtain singular spectrum analysis of the Hankel matrix based on the L1 and L2 mixed norm; and alternating minimization and weighted least square minimization are performed on singular spectrum analysis in turn to solve the factor matrix of the Hankel matrix of the single frequency seismic data so as to obtain estimation of the Hankel matrix of the single frequency seismic data and complete noise reduction of the single frequency seismic data. The seismic irregular noise removing method based on robust singular spectrum analysis can be widely applied to the field of seismic data processing.

Description

technical field [0001] The invention relates to a seismic irregular noise removal method based on robust singular spectrum analysis, and belongs to the field of seismic data processing. Background technique [0002] Noise suppression of seismic data is an important process in seismic data processing. There are different denoising methods for different types of seismic interference, but the biggest challenge is still how to achieve amplitude-preserving denoising and whether amplitude-preserving denoising can be achieved It mainly depends on the degree of effective separation of mixed signals and interferences. The harmonic analysis method projects seismic data from the original recording space to different orthogonal subspaces through transformation processing such as rotation, stretching, and translation. Personnel provide another perspective for analyzing and processing seismic data. Therefore, how to design targeted feature functions or feature records according to the cha...

AI Technical Summary

Problems solved by technology

After adopting the robust norm, the rank reduction filter based on singular spectrum analysis is also used to suppress the coherent noise. The truncated SVD method, or the truncated SVD method based on a robust norm (such as the turkey norm), its core problem is that the truncated SVD method based on the L2 norm cannot really effectively remove irregular noise, because the core essence of the L2 norm is average

In the truncated SVD method based on the turkey norm, the turkey norm is a concave function, and its optimization problem is essentially a non-convex optimization, which cannot find a global optimal solution and intelligently obtain a local optimal solution, and this method relies on the initial value, which will lead to Affects the performance of noise reduction

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