Rank reduction and denoising method for singular value attenuation

Abstract

The invention discloses a rank reduction and denoising method for singular value attenuation. The method is applied to the seismic data processing field. In the prior art, noisy seismic data cannot bewell decomposed into a noise subspace and a signal subspace, the effect is not ideal under the condition of low signal-to-noise ratio, for the above problem, the invention provides the method which comprises the following steps: firstly, calculating optimized weight coefficients of left and right singular vectors of a noisy observation matrix; using the characteristic that an optimized weight coefficient only depends on limit singular value distribution of a pure noise matrix to decompose the noisy signal well into the noise subspace and the signal subspace, then adding a regularization operator to constrain the singular value on the above basis so as to obtain more stable low-rank estimation, and finally, obtaining stable estimation of seismic data in a frequency domain by utilizing reverse diagonal average processing. The method has outstanding denoising capability in the application of suppressing seismic random noise.

Description

technical field [0001] The invention belongs to the field of seismic data processing, in particular to a seismic denoising technology. Background technique [0002] With the continuous deepening of oil and gas exploration and development, the geological structure and geological environment of the exploration area are becoming more and more complex and changeable. In the seismic data collected in the middle and deep exploration target areas on land and offshore, due to the influence of complex geological conditions and other factors, the quality of the original data is usually relatively poor, mainly manifested in the presence of serious random noise and coherent noise in the profile Interference, the effective reflection signal is weak and covered by strong noise, and the signal-to-noise ratio and fidelity of the data are low. Since the signal-to-noise ratio of seismic data will directly affect the accuracy of geological interpretation, seismic data denoising processing has...

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Problems solved by technology

However, TSVD only achieves the purpose of rank reduction and denoising by truncating some of the smaller singular values, and it cannot decompose the noisy seismic data into noise subspace and signal subspace well, and the effect is not good in the case of low signal-to-noise ratio. very ideal

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