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Small-scale threshold denoising method based on wavelet transform

A technology of threshold denoising and wavelet transform, which is applied in the field of small-scale threshold denoising based on wavelet transform, and can solve problems such as loose interpretation

Active Publication Date: 2014-01-29
CHINA PETROLEUM & CHEM CORP +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0013] The inner product in (4) is often loosely interpreted as convolution

Method used

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  • Small-scale threshold denoising method based on wavelet transform
  • Small-scale threshold denoising method based on wavelet transform
  • Small-scale threshold denoising method based on wavelet transform

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Embodiment Construction

[0059] The present invention is further described below in conjunction with embodiment. The scope of the present invention is not limited by these examples, and the scope of the present invention is set forth in the claims.

[0060] Such as figure 1 It is a flow chart of wavelet transform small-scale threshold denoising, which shows the implementation process of this method technology. Specific detailed implementation steps:

[0061] The specific implementation steps of denoising are as follows:

[0062] Let the collected noisy seismic signal be s i (t), N channels, M sampling points, the sampling rate is 1ms, where i is the number of channels, and t is the time. Take a time window with a size of L×M, and calculate the correlation coefficient cor(s) of the data in the L channel L (t)), assuming that δ is the critical correlation coefficient whether it is signal-based or noise-based, as in Equation 4, then

[0063]

[0064] The signal-based seismic trace adopts the fre...

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Abstract

The invention belongs to the technical field of digital signal processing, such as seismic data processing, and particularly relates to a mall-scale threshold denoising method based on wavelet transform; and the theoretical basis is provided for selecting frequency dividing parameters and a proper wavelet threshold during wavelet transform denoising. The method includes firstly scanning seismic data in a small-scale manner, and acquiring a relevant coefficient in a time window; secondly setting a threshold, judging whether the data in the small-sized time window are mainly seismic signals or noise signals, and then adopting proper frequency dividing parameters and a wavelet threshold to denoise; when the seismic channels are mainly of the seismic signals, adopting conventional wavelet decomposition and a conventional hard threshold or soft threshold; when the seismic channels are mainly of the noise signals, performing wavelet packet decomposition on the seismic signals, and setting a following floating threshold method by adopting the best entropy principle; finally performing wavelet reconstruction on denoised wavelet scales, and acquiring a seismic channel set with higher noise ratio after denoising.

Description

technical field [0001] The invention relates to the technical field of digital signal processing such as seismic exploration data processing, and in particular to a small-scale threshold denoising method based on wavelet transform. Background technique [0002] Random noise is a type of noise that is difficult to eliminate in seismic exploration. The main conventional methods for removing random noise include band-pass filtering, median filtering, F-X deconvolution, and conventional wavelet thresholding. These methods have a good effect on removing random noise, but they all have the problem of unclean noise removal or damage to effective waves to varying degrees. [0003] Wavelet transform threshold denoising method Denoising technology is widely used in seismic signal processing because of its relatively simple algorithm theory, convenient implementation and good denoising effect. In conventional wavelet threshold denoising processing, a fixed frequency division number i...

Claims

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Application Information

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IPC IPC(8): G01V1/36
Inventor 谢金娥刘志成贾春梅
Owner CHINA PETROLEUM & CHEM CORP
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