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Non-homogeneous curvelet three-dimensional earthquake data reconstruction method based on linear Bregman algorithm

A three-dimensional technology of seismic data and curvelet, which is applied in seismology, seismic signal processing, geophysical measurement, etc., can solve the problems of not being widely used, and the reconstruction method of two-dimensional non-uniform curvelet transform with missing seismic traces is not high in accuracy. , to achieve a wide range of applications, less computational workload, and continuous events

Inactive Publication Date: 2017-07-21
EAST CHINA UNIV OF TECH
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Problems solved by technology

[0007] The present invention proposes a non-uniform curvelet three-dimensional seismic data reconstruction method based on the linear Bregman algorithm. The transformation reconstruction method has low precision and is not widely used.

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  • Non-homogeneous curvelet three-dimensional earthquake data reconstruction method based on linear Bregman algorithm
  • Non-homogeneous curvelet three-dimensional earthquake data reconstruction method based on linear Bregman algorithm
  • Non-homogeneous curvelet three-dimensional earthquake data reconstruction method based on linear Bregman algorithm

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Embodiment 1

[0065] The steps to implement this method mainly include: establishment of seismic data reconstruction model, conventional two-dimensional curvelet forward and reverse transformation, non-uniform fast Fourier transform, establishment of non-uniform curvelet forward and reverse transformation operator, seismic wave field reconstruction, linear Bregman algorithm solution Wait. Specific steps are as follows:

[0066] Step 1: Data reconstruction model establishment. The problem of seismic data reconstruction is to recover complete seismic data from incomplete data, assuming the following linear forward modeling model

[0067] y=Md

[0068] where y∈R m Represents the incomplete seismic data collected; d∈R n , and n>>m, represents the complete data to be reconstructed; M∈R n×m Represents a randomly sampled matrix. Assuming that the data x is a sparse representation of d in the curvelet transform domain C, the above equation can be written as:

[0069] y=Ax and

[0070] Her...

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Abstract

The invention discloses a non-homogeneous curvelet three-dimensional earthquake data reconstruction method based on a linear Bregman algorithm. The method is characterized by comprising steps of firstly successively extracting time slices of non-homogeneous three-dimensional earthquake data in two space directions; based on multi-dimension multi-direction two-dimensional curvelet positive conversion, introducing two-dimensional space non-homogeneous rapid Fourier transformation; establishing non-homogeneous curvelet inverse conversion operators between earthquake missing data under homogeneous curvelet coefficients and space non-homogeneous sampling; using the linear Bregman algorithm to carry out resolving; by selecting proper threshold value factors and dynamic step lengths, and adopting soft threshold value operators, carry out precise back calculation to obtain homogeneous curvelet coefficients of irregular earthquake data under non-homogeneous sampling; and finally, carrying out normal curvelet inverse transformation, thereby forming a new reconstruction method. According to the invention, the resolution ratio and the signal to noise ratio of reconstructed signals are greatly improved; and the method has important value in aspects of guiding acquisition of non-homogenous earthquake data and reconstruction of missing roads in complex regions.

Description

technical field [0001] The invention relates to a seismic data reconstruction method for irregular missing traces under spatial non-uniform sampling, in particular to a non-uniform curve wave three-dimensional seismic data reconstruction method based on a linear Bregman algorithm. [0002] technical background [0003] In the process of field data collection, in order to obtain relatively complete and regular seismic data, the field observation system must be designed before data collection. It usually deviates from the original design position, and even some shot receivers cannot collect effective seismic data, resulting in irregular undersampling of seismic data along the spatial direction, resulting in spatial aliasing, which affects the effect of other subsequent processing methods and reduces It improves the resolution of seismic exploration (Trad, 2009; Zhang et al., 2013). In order to solve this problem, the most direct and effective method is to re-collect data in th...

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Application Information

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IPC IPC(8): G01V1/32
CPCG01V1/32G01V2210/48
Inventor 张华杨海燕李红星
Owner EAST CHINA UNIV OF TECH
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