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Fractional-Fourier-transform-based random noise suppression method

A fractional Fourier, random noise technology, applied in the field of seismic exploration data processing, can solve problems such as sacrificing effective signals

Active Publication Date: 2011-10-19
BC P INC CHINA NAT PETROLEUM CORP +1
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Problems solved by technology

In actual data processing, a compromise method is usually adopted, that is, while removing noise, part of the effective signal will also be sacrificed

Method used

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  • Fractional-Fourier-transform-based random noise suppression method

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Embodiment Construction

[0022] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, the same reference numerals denote the same components.

[0023] The present invention introduces the method of fractional Fourier transform (FRFT) into the quasi-linear F-X domain prediction filtering technology, transforms the two-dimensional seismic data into any fractional space domain, and performs prediction processing, which can Realize the best compromise between predictive filtering effect and signal feature preservation.

[0024] figure 1 is a flowchart of a random noise suppression method based on fractional Fourier transform according to an exemplary embodiment of the present invention.

[0025] refer to figure 1 , in step 101, for the collected seismic data, using the orthogonal polynomial approximation method and adopting the maximum similarity coefficient criterion to calculate the correlation coefficient describin...

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Abstract

The invention provides a fractional-Fourier-transform-based random noise suppression method, which comprises the following steps of: calculating related coefficients of the tendency of an in-phase axis in seismic data for the acquired seismic data by utilizing an orthogonal polynomial approximation method and adopting maximum similarity coefficient criterion; performing pseudo-linear spatial transformation on the seismic data by utilizing the calculated related coefficients; calculating an optimal order of a fractional domain with the most centralized signals in the seismic data according to a fractional-Fourier-transform-based optimal order calculation method; transforming the seismic data from a time-space domain to the fractional domain by utilizing fractional Fourier transform under the optimal order; resolving a prediction operator of a seismic channel, and predicting the seismic data of the seismic channel by utilizing the prediction operator to eliminate irregularly distributedrandom noises; transforming the denoised seismic data from the fractional domain to the time-space domain by utilizing the inverse fractional Fourier transform; and performing inverse linear transformation on the seismic data by utilizing the calculated related coefficients to finish the random noise suppression of the seismic data.

Description

technical field [0001] The invention relates to petroleum seismic exploration and belongs to the field of seismic exploration data processing. More specifically, the present invention relates to a method for suppressing random noise in low signal-to-noise ratio data to improve the signal-to-noise ratio of seismic data, which can achieve the best compromise between denoising effect and signal fidelity . Background technique [0002] Seismic exploration is a geophysical exploration method that uses the difference in elasticity and density of underground media to infer the nature and shape of underground rock formations by observing and analyzing the response of the earth to artificially induced seismic waves. Seismic prospecting is an important means of surveying oil and natural gas resources before drilling, and it is also widely used in coal field and engineering geological exploration, regional geological research and crustal research. In seismic exploration, the most bas...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01V1/28
Inventor 张华陈爱萍何光明曹中林罗红明吕文彪王玉华
Owner BC P INC CHINA NAT PETROLEUM CORP
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