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Self-adaption wavelet threshold solving method

A wavelet threshold and self-adaptive technology, applied in seismic signal processing, etc., can solve problems such as signal oscillation, affecting the approximation degree between reconstructed signal and real signal, and achieve strong pertinence and obvious denoising effect

Active Publication Date: 2015-01-14
CHINA PETROLEUM & CHEM CORP +1
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Problems solved by technology

For example, in the hard-threshold method, the wavelet coefficients are discontinuous at ±λ, and the reconstructed signal may produce some oscillations; although the wavelet coefficients estimated by the soft-threshold method have good overall continuity, c′( There is always a constant deviation between i, j) and c(i, j), which directly affects the approximation between the reconstructed signal and the real signal

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Embodiment Construction

[0035] Below in conjunction with accompanying drawing, the present invention is described in further detail:

[0036] The present invention provides an adaptive threshold calculation method for wavelet transform denoising, and the specific implementation steps are:

[0037] (1) Classic wavelet transform threshold denoising method

[0038] Let x(t) be a square-integrable function denoted as (x(t)∈L 2 (R)), is a function of the basic wavelet or mother wavelet. but

[0039]

[0040] Called the wavelet transform of x(t). In the formula, a>0 is the scale factor, and τ is the reflection displacement, and its value can be positive or negative. The symbol represents the inner product, and its meaning is (the superscript * represents the conjugate)

[0041] =∫x(t), y * (t)dt (2)

[0042] is the displacement and scaling of the basic wavelet. In formula (1), not only t is a continuous variable, but a and τ are also continuous variables, so it is called continuous wavelet ...

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Abstract

The invention provides a self-adaption wavelet threshold solving method, and belongs to the field of seismic exploration data processing and other digital signal processing. The method includes the steps of firstly, inputting a noisy signal gather fi(t); secondly, solving a self-adaption weighted stack (please see the symbol in the specification); thirdly, solving a self-adaption variance sigma[i, new] (t) in an iteration mode point by point, judging whether the difference between the sigma[i, new] (t) and sigma[i-1, new] (t) is smaller than xi or not, if yes, executing the fourth step, and if not, replacing the existing variance with a new variance sigma [i, new] (t) and then executing the second step again; fourthly, substituting the self-adaption variance into a threshold solving function to obtain a threshold lambda; fifthly, conducting wavelet domain denoising on W[j, k] through the threshold lambda; sixthly, conducting wavelet reconstruction on the denoised symbol (please see the symbol in the specification) to obtain seismic data generated after wavelet reconstruction; seventhly, outputting the seismic data obtained in the sixth step and generated after wavelet reconstruction. Through a theoretical model and an actual seismic data test, the method is remarkable in denoising effect and is highly purposeful.

Description

technical field [0001] The invention belongs to the field of digital signal processing such as seismic exploration data processing, and in particular relates to an adaptive wavelet threshold calculation method. Background technique [0002] Wavelet analysis is a rapidly developing new field in the current mathematical research, and it has dual meanings of deep theory and wide application. The traditional Fourier transform is global, it describes the overall nature of the signal, and the wavelet analysis has good time-frequency locality, it can decompose the information carried by the signal into any details for analysis, and the signal and noise in the wavelet transform Detailed information has completely different characteristics, so it can be applied to seismic signal denoising. So far, wavelet denoising has been successfully applied to surface wave suppression and random noise suppression. Wavelet transform can effectively extract information from signals, perform multis...

Claims

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Application Information

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