Seismic signal restoration algorithm based on tensor nuclear norm regularization

A seismic signal and algorithm technology, applied in seismic signal processing and other directions, can solve problems such as the inability to make good use of multi-dimensional data correlation

Active Publication Date: 2018-02-23
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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The methods for solving tensor decomposition generally include CP decomposition, Tucker decomposition and HOSV

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  • Seismic signal restoration algorithm based on tensor nuclear norm regularization
  • Seismic signal restoration algorithm based on tensor nuclear norm regularization
  • Seismic signal restoration algorithm based on tensor nuclear norm regularization

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[0099] The present invention transforms the problem of restoring pre-stack seismic signals into the problem of tensor filling, utilizes the low rank property of seismic data tensors to construct tensor kernel norms, thereby effectively reconstructing missing seismic data. For solving the tensor filling problem, the present invention adopts the iterative decomposition method of ADMM, which extracts an unknown variable each time and fixes other variables, the calculation process is relatively simple, and the theoretical basis is strong. Compared with updating three parameters at a time, ADMM The algorithm converges faster and the effect is better. At the same time, for the constructed Hankel tensor nuclear norm, this paper proposes the HTR-SVD decomposition method based on the tensor random singular value decomposition (TR-SVD), and combines the truncation method of the matrix rank in the damped MSSA to construct the rank Damping cutoff selection method. The technical solutions...

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Abstract

The invention discloses a seismic signal restoration algorithm based on tensor nuclear norm regularization. The method comprises the following steps: S1, constructing an objective function based on Hankel tensor nuclear norm regularization; and S2, solving the objective function by using an ADMM algorithm. According to characteristics of correlation, redundancy and low rank of the pre-stack seismic signal, the invention provides a Hankel tensor nuclear norm regularization method to reconstruct missing seismic data. With a HTR-SVD decomposition method, a Hankel matrix construction mode is applied to a tensor nuclear norm, so that the correlation of a tensor data block is increased, the low-rank performance of the tensor data block is improved, and a problem that a pre-stack seismic signal can not be restored because of lack of a tangent plane is solved; and thus work area data selected randomly can be processed by the method, so that the the robustness and adaptability of processing themissing seismic data are improved. While filling of missing data is carried out, noises in signals are suppressed, so that the signal to noise ratio of the signals is increased.

Description

technical field [0001] The invention belongs to the technical field of seismic data signal processing, in particular to a seismic signal recovery algorithm based on tensor kernel norm regularization. Background technique [0002] With the rapid development of the world economy, the consumption of resources by industry and agriculture in various countries has also increased sharply, and the demand for non-renewable energy such as oil and natural gas has continued to increase. Oil and natural gas resources are the foundation of the industrial economy. Whether their effective supply can be guaranteed is related to all aspects of national life, thereby affecting the stability of the country's internal stability and order, so it is necessary to ensure that its supply cannot be interrupted; for this reason, like the United States Energy-consuming countries such as Japan and Japan have established oil storage mechanisms to deal with possible crises. [0003] The oil storage in the...

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Application Information

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IPC IPC(8): G01V1/36
CPCG01V1/36
Inventor 钱峰陈全张飞笼胡光岷
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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