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
View PDF10 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the conventional redundant dictionary learning method is often too rough, an

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 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

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap