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:
Oct. 10, 2023

Filed:

Jun. 14, 2022
Applicant:

Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, CN;

Inventors:

Zhongxing Wang, Beijing, CN;

Lili Kang, Beijing, CN;

Zhiguo An, Beijing, CN;

Ruo Wang, Beijing, CN;

Xiong Yin, Beijing, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G01V 3/08 (2006.01); G01V 99/00 (2009.01); G06N 3/08 (2023.01);
U.S. Cl.
CPC ...
G01V 99/005 (2013.01); G01V 3/087 (2013.01); G01V 3/088 (2013.01); G06N 3/08 (2013.01);
Abstract

Disclosed is a magnetotelluric inversion method based on a fully convolutional neural network. The magnetotelluric inversion method includes: constructing a multi-dimensional geoelectric model; constructing a fully convolutional neural network structure model to obtain initialized fully convolutional neural network model parameters; training and testing the fully convolutional neural network structure model based on the training sets and the test sets to obtain optimized fully convolutional neural network structure model parameters; determining whether training of the fully convolutional neural network structure model is completed according to fitting error changes corresponding to the training sets and the test sets; and finally, inputting measured apparent resistivity into a trained fully convolutional neural network structure model for inversion, and further optimizing the fully convolutional neural network structure model by analyzing precision of an inversion result until an inversion fitting error satisfies a set error requirement.


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