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Self-adaptive anisotropic divided frequency partition filtering method based on energy frequency band distribution

An anisotropic and partitioned filtering technology, which is applied in the direction of measuring devices, geophysical surveys, instruments, etc., can solve the problems of difficult and insufficient threshold selection of CL models, so as to improve denoising and boundary restoration capabilities, improve quality, and filter out The effect of noise protection

Active Publication Date: 2018-06-29
CHENGDU UNIVERSITY OF TECHNOLOGY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a solution to the above problems, overcome the difficulty of CL model threshold selection and the insufficient processing in the whole frequency band, and improve the denoising and boundary of anisotropic filtering in multi-scale noise environment Adaptive Anisotropic Frequency Division Partition Filtering Method Based on Energy Band Distribution for Resilience

Method used

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  • Self-adaptive anisotropic divided frequency partition filtering method based on energy frequency band distribution
  • Self-adaptive anisotropic divided frequency partition filtering method based on energy frequency band distribution
  • Self-adaptive anisotropic divided frequency partition filtering method based on energy frequency band distribution

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Embodiment 1

[0041] Embodiment 1: see figure 1 , an adaptive anisotropic frequency division and partition filtering method based on energy frequency band distribution, comprising the following steps:

[0042] (1) Input two-dimensional post-stack seismic data u;

[0043] (2) Use VMD to decompose the two-dimensional post-stack seismic data in the frequency domain to obtain IMF profiles u in different frequency ranges k (k=1,2,...,n), n is an integer; in step (2), the method for VMD to decompose the two-dimensional post-stack seismic data in the frequency domain is,

[0044] Calculate u according to the following formula k (k=1,2,...,n)

[0045]

[0046] Among them, δ(t) is the Dirac function, ω k Indicates the center frequency of each frequency band range;

[0047] (3) Each decomposed IMF profile u k (k=1,2,...,n) are respectively subjected to multiple iterative processing through an adaptive threshold anisotropic filtering algorithm, and a reconstructed final result u is obtained a...

Embodiment 2

[0082] Such as figure 2 The artificially synthesized two-dimensional graben geological model is shown, in which the model contains 250 traces, each trace contains 490 sampling points, and the sampling frequency is 1ms. Two main faults are designed in the figure, and each fault contains breakpoints with different fault throws.

[0083] Such as image 3 as shown, image 3 yes figure 2 Section of the graben model after adding random Gaussian noise with a signal-to-noise ratio of -1db. Due to the influence of noise, the distance and breakpoints become blurred and difficult to distinguish clearly.

[0084] Figure 4 for will image 3 The sectional view after being processed for the method of the present invention; From Figure 4 The upper part and strata are visible, the breakpoints are recovered clearly, the lateral boundaries are clear, the weak amplitude is strengthened, the signal-to-noise ratio is also increased from -1dB to 13.4575dB, and the similarity is increased ...

Embodiment 3

[0086] Such as Figure 5 , Image 6 Shown are local raw actual data profiles and their processed profiles. Figure 5 It can be seen that the actual data often contain different types of noise. Due to the existence of noise, many weak amplitudes are covered by noise, small fault throws disappear, and the lateral continuity of formations is poor. Figure 5 and Image 6 It can be seen from the comparison that after being processed by the method of the present invention, the weak amplitude covered by the noise is restored and strengthened, the fault distance is clear, and the lateral continuity of the formation is strengthened.

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Abstract

The invention discloses a self-adaptive anisotropic divided frequency partition filtering method based on energy frequency band distribution. The self-adaptive anisotropic divided frequency partitionfiltering method comprises the following steps: (1) inputting two-dimensional post-stack seismic data u; (2) performing frequency domain decomposition on the two-dimensional post-stack seismic data byutilizing VMD to obtain IMF profiles uk (k is equal to 1, 2 to n) within different frequency ranges, wherein n is an integer; (3) respectively performing multiple iterative processing on each of thedecomposed IMF profiles uk (k is equal to 1, 2 to n) through a self-adaptive threshold anisotropic filtering algorithm and obtaining final refactoring results after each iterative processing and respectively calculating signal-to-noise ratio SNR and similarity SSIM; (5) selecting the optimal final result corresponding to the SNR and the SSIM for output. The self-adaptive anisotropic divided frequency partition filtering method disclosed by the invention has the benefits that de-noising is carried out in combination with the characteristics of a signal in a frequency domain and a time domain, local features and main structure information of seismic data textures are better protected while the noise is filtered, moreover, the seismic data quality is improved.

Description

technical field [0001] The invention relates to a seismic data analysis and processing method, in particular to an adaptive anisotropic frequency division and partition filtering method based on energy frequency band distribution. Background technique [0002] Seismic data collected in the field contains useful information related to subsurface structure and lithology, and the discontinuity of subsurface structure and formation edge information often indicate the possibility of oil and gas reservoirs there. However, in the process of seismic data acquisition, it is often disturbed by external noise, which distorts or even conceals the boundary and fault information, resulting in a decrease in the quality of seismic data, which is not suitable for direct geological interpretation. Therefore, it is necessary to filter the seismic data first. deal with. In continuous research, it has been found that the intensity of noise distribution in different frequency ranges is different...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01V1/28G01V1/36
CPCG01V1/28G01V1/364G01V2210/21G01V2210/324
Inventor 陈辉冯俊陈元春胡英郭科王洪辉
Owner CHENGDU UNIVERSITY OF TECHNOLOGY
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