Curvelet transform-based denoising method of seismic signals

A seismic signal and seismic recording technology, applied in seismic signal processing and other directions, can solve the problems of poor seismic data processing effect, slow calculation speed, complicated process, etc., to achieve good matched filtering effect, suppress noise, and high consistency. Effect

Inactive Publication Date: 2015-06-24
JILIN UNIV
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Problems solved by technology

[0006] Although the above-mentioned prior art can effectively suppress the noise to a certain extent, the process is complex, the calculation speed is slow, the signal-to-noise ratio is low, and the seismic data processing effect is not very good.

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  • Curvelet transform-based denoising method of seismic signals
  • Curvelet transform-based denoising method of seismic signals
  • Curvelet transform-based denoising method of seismic signals

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Embodiment Construction

[0041] Below in conjunction with accompanying drawing and embodiment the present invention will be described in further detail:

[0042] Such as figure 1 It is a flow chart of seismic signal denoising based on Curvelet transform,

[0043] The seismic signal denoising method based on Curvelet transform comprises the following steps:

[0044] A. Input the noise-containing signal gather;

[0045] B. Using Curvelet transform, the matching operator A is obtained from the original seismic data;

[0046] C. Utilize the matching operator A to construct the equation Ax=b+n...(1),

[0047] In the matched filtering process: A is the matching operator, x is the data to be matched, b is the matching channel data, and n is random noise;

[0048] In the process of suppressing random noise: A represents the operator matrix that generates random noise, x is the data without random noise, and b is the seismic record with random noise; n in the above two cases is unknown noise data;

[0049...

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Abstract

The invention relates to a Curvelet transform-based denoising method of seismic signals. According to the method, a wedge model of different wavelet lengths is established, only one lamina is in the wedge model, different wavelet resolution laminas and wedges are different in capacity, a seismic record of low time resolution is used as a matching channel, a seismic record of high time resolution is used as a reference channel, the seismic record, namely the matching channel, and the seismic record doped with white Gaussian noise are matched by means of Curvelet transform matching and filtering and are compared with the seismic record of high reference channel resolution. Actual comparison tests show that by processing the influences of the aspects by the FITSA algorithm, matching results are high in waveform uniformity, noise is well suppressed, and seismic data processing resolution and signal-to-noise ratio are evidently increased; the signal-to-noise ratio is increased, and processing efficiency of the seismic data is greatly improved.

Description

technical field [0001] The invention relates to a method for processing seismic data, in particular to a seismic signal denoising method based on Curvelet transformation. technical background [0002] In seismic exploration, the seismic data of different sources are limited by the matched filtering method, and there are still a series of differences in the seismic data after the matched filtering, such as low signal-to-noise ratio and low resolution; clear, which makes it difficult to guarantee the accuracy of wave impedance inversion and the extraction of seismic attribute parameters; or there are discontinuous events and low signal-to-noise ratio in the splicing of seismic data from different blocks, which brings difficulties to seismic data matching processing. These series of problems seriously restrict the resolution and signal-to-noise ratio of seismic data processing. [0003] Jilin University’s 2008 master’s thesis published "Research on Seismic Data Denoising Metho...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01V1/28
Inventor 龙云王洪超陈祖斌林君杨泓渊
Owner JILIN UNIV
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