The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 18, 2022

Filed:

Jul. 08, 2021
Applicant:

Xi'an Jiaotong University, Xi'an, CN;

Inventors:

Jinghuai Gao, Xi'an, CN;

Hongling Chen, Xi'an, CN;

Zhaoqi Gao, Xi'an, CN;

Chuang Li, Xi'an, CN;

Lijun Mi, Xi'an, CN;

Jinmiao Zhang, Xi'an, CN;

Qingzhen Wang, Xi'an, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01V 1/28 (2006.01); G01V 1/36 (2006.01);
U.S. Cl.
CPC ...
G01V 1/282 (2013.01); G01V 1/364 (2013.01); G01V 2210/32 (2013.01); G01V 2210/665 (2013.01);
Abstract

A model-driven deep learning-based seismic super-resolution inversion method includes the following steps: 1) mapping each iteration of a model-driven alternating direction method of multipliers (ADMM) into each layer of a deep network, and learning proximal operators by using a data-driven method to complete the construction of a deep network ADMM-SRINet; 2) obtaining label data used to train the deep network ADMM-SRINet; 3) training the deep network ADMM-SRINet by using the obtained label data; and 4) inverting test data by using the deep network ADMM-SRINet trained at step 3). The method combines the advantages of a model-driven optimization method and a data-driven deep learning method, and therefore the network has the interpretability; and meanwhile, due to the addition of physical knowledge, the iterative deep learning method lowers requirements for a training set, and therefore an inversion result is more reliable.


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