Sparse representation regularization pre-stack AVO inversion method based on fast orthogonal dictionary

A sparse representation and dictionary technology, applied in the field of geophysical exploration and interpretation, can solve problems such as limiting the application of algorithms, iterative increase in the dictionary learning process, and redundant overlapping of small blocks, so as to improve the sparse inversion rate, promote development and application, and parameter Adjust the effect of convenience

Active Publication Date: 2020-07-03
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

At present, the dictionary inversion method based on KSVD assumes that the dictionary atoms are redundant, that is, there is information overlap between the atoms. Using dictionary atoms with high redundancy in this algorithm can improve the sparsity of the representation coefficients, b

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  • Sparse representation regularization pre-stack AVO inversion method based on fast orthogonal dictionary
  • Sparse representation regularization pre-stack AVO inversion method based on fast orthogonal dictionary
  • Sparse representation regularization pre-stack AVO inversion method based on fast orthogonal dictionary

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Embodiment 1

[0090] In order to evaluate the pre-stack AVO inversion rate based on the regularization constraint promoted by orthogonal dictionary sparsity of the present invention, and check its inversion effect. After sampling and time-depth conversion processing, the Marmousi model with a time domain size of 200×400, a sampling interval of 4 ms and a Reich wavelet with a main frequency of 25 Hz were selected to test the proposed algorithm, as shown in figure 2 (a) shows the true P-wave velocity, such as figure 2 (b) shows the shear wave velocity, such as figure 2 (c) shows the density profile. Consistent with the inversion method of KSVD dictionary sparse constraints based on redundancy assumptions, we selected the same 20-well data from the 400-track data of the Marmousi model to take small blocks as training samples. In this embodiment, the orthogonal dictionaries of P-wave velocity, S-wave velocity and density used for inversion constraint are respectively D P 、D S and D ρ , ...

Embodiment 2

[0095] In order to test the practical applicability of the proposed method, the method of the present invention is applied to the inversion of pre-stack actual data in a certain work area in China. The size of the time domain of the actual data is 80×853, and the training dictionary process uses 13 The well data are low-pass filtered and small blocks are taken to form training samples. Such as image 3 Shown is the comparison of the inversion results based on the two dictionaries, where image 3 (a) is the P-wave constrained inversion by the KSVD dictionary (dictionary size is 50×700), image 3 (b) is the shear wave constrained inversion by the KSVD dictionary (dictionary size is 50×700), image 3 (c) is the KSVD dictionary (dictionary size is 50×700) constrained density inversion profile, image 3 (d) is the P-wave constrained inversion by the ORTD dictionary (dictionary size is 50×700), image 3 (e) is the shear wave constrained inversion by the ORTD dictionary (dictiona...

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Abstract

The invention discloses a sparse representation regularization pre-stack AVO inversion method based on a fast orthogonal dictionary. The method is applied to the technical field of geophysical exploration interpretation, and in order to solve the problems that an existing KSVD dictionary inversion method is low in operation rate and large in number of controlled parameters, an objective function is constructed according to known angle gather seismic data, wavelet sequences, incident angle information and initial model parameters; adding regularization constraints; and finally, iteratively solving the regularization constraint objective function based on the orthogonal dictionary to obtain final model parameters. According to the method, the inversion effect equivalent to that of a KSVD dictionary method is guaranteed, and meanwhile the sparse inversion rate can be effectively increased.

Description

technical field [0001] The invention belongs to the technical field of geophysical exploration interpretation, in particular to a pre-stack AVO inversion technology using orthogonal dictionary learning combined with sparse representation regularization constraints. Background technique [0002] AVO (Amplitude variation with offset, amplitude variation with offset) inversion attempts to convert the pre-stack amplitude into an effective elastic parameter that reflects the lithology and oil and gas information of the subsurface medium, and is currently one of the most important reservoir prediction methods. Due to lack of prior information, serious noise interference of data, and many inversion parameters, AVO inversion has the characteristics of multi-solution and instability. Reducing the non-uniqueness and instability of inversion results is the core issue of inversion algorithms (Sa et al, 2015), and regularization technology is one of the most commonly used methods to impr...

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Application Information

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IPC IPC(8): G06F17/11G06F17/16G01V1/28G01V1/30
CPCG01V1/282G01V1/301G01V1/306G06F17/11G06F17/16
Inventor 王峣钧刘宇厍斌胡光岷
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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