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Seismic signal restoration method based on dictionary learning regularization sparse representation

A dictionary learning, sparse representation technology, applied in seismic signal processing, seismology, scientific instruments, etc., can solve the problems of inaccurate seismic data, difficult to meet production needs, roughness, etc.

Pending Publication Date: 2018-01-09
OPTICAL SCI & TECH (CHENGDU) LTD
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Problems solved by technology

Therefore, the conventional redundant dictionary learning method is often too rough, and the seismic data obtained are not accurate enough to meet the actual production needs

Method used

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  • Seismic signal restoration method based on dictionary learning regularization sparse representation
  • Seismic signal restoration method based on dictionary learning regularization sparse representation
  • Seismic signal restoration method based on dictionary learning regularization sparse representation

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Embodiment Construction

[0085] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0086] Such as figure 1 As shown, the seismic signal recovery method based on dictionary learning regularized sparse representation includes the following steps:

[0087] S1. Apply the tensor product method to the tensor dictionary learning process to construct the objective function; the specific implementation method is as follows: sparse coding is to approximate the input vector through the linear combination of some basis vectors, and the combination of basis vectors can effectively Extract the main features of the input data. For each input vector y∈R n Using the sparse vector a 1 ,a 2 ,...,a p ∈ R n Indicates that the coefficient x∈R of the sparse vector n , so y≈∑ j a j x j ; The error y-∑ between the input vector and its sparse vector expression j a j x j Gaussian distribution with zero mean and covariance σ;

[0088] For...

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Abstract

The invention discloses a seismic signal restoration method based on dictionary learning regularization sparse representation. The method comprises the steps that S1 a tensor product method is appliedto a tensor dictionary learning process to construct an objective function; S2 an alternating iterative algorithm is used to solve a tensor sparse coefficient; S3 a tensor dictionary is trained by the Lagrange dual method; and S4 the tensor dictionary and the tensor sparse coefficient are iteratively updated to reconstruct a missing seismic signal. According to the seismic signal restoration method based on dictionary learning regularization sparse representation, firstly a t-product operator is introduced into tensor decomposition, and accordingly a new objective function is constructed; the ADM algorithm and the Lagrange dual algorithm are used to solve the sparse coefficient and the tensor dictionary; sparse coding of the seismic signal is realized; sparse representation of tensor data and recovery of the missing seismic signal are finally realized; and the effect of seismic data reconstruction is improved.

Description

technical field [0001] The invention belongs to the technical field of seismic signal processing, in particular to a seismic signal recovery method based on dictionary learning regularized sparse representation. Background technique [0002] With the deepening of exploration and development of unconventional energy such as coalbed methane and shale gas, seismic exploration puts forward higher requirements for the regularity and integrity of data. However, in the process of field seismic acquisition, the obstacles of mountains, rivers or lakes make it very difficult to place the geophones; near cities and villages, the existence of buildings also makes it difficult for us to place them in the corresponding positions. The receiving device; at the same time, due to the loss of the transmitting and receiving equipment, the underground information cannot be collected in some places, resulting in the loss of some data. Partial absence of pre-stack seismic signals leads to disconn...

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Application Information

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IPC IPC(8): G01V1/00G01V1/28G06F17/16
Inventor 钱峰畅京博张飞笼胡光岷
Owner OPTICAL SCI & TECH (CHENGDU) LTD
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