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:
Aug. 11, 2026

Filed:

Jun. 09, 2021
Applicant:

Cgg Services Sas, Massy Cedex, FR;

Inventors:

Hanyuan Peng, Paris, FR;

Jeremie Messud, Palaiseau, FR;

Céline Lacombe, Massy, FR;

Paulien Jeunesse, Antony, FR;

Assignee:

CGG SERVICES SAS, Massy Cedex, FR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01V 1/38 (2006.01); G01V 1/30 (2006.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01);
U.S. Cl.
CPC ...
G01V 1/3808 (2013.01); G01V 1/30 (2013.01); G01V 1/3843 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G01V 2210/1293 (2013.01); G01V 2210/56 (2013.01);
Abstract

A DUnet engine produces a processed image of seismic data acquired over an underground formation. The DUnet engine includes: a contractive path that performs multilayer convolutions and contraction to extract a code from the seismic data input to the DUnet, an expansive path configured to perform multilayer convolutions and expansion of the code, using features provided by the contractive path through skip connections, and a model level that performs multilayer convolutions on outputs of the contractive path and expansive paths to produce the processed image and/or an image that is a difference between the processed image and the seismic data. A fraction of the seismic data may be selected for training the DUnet engine using an anchor method that automatically extends an initial seismic data subset, based on similarity measurements. A reweighting layer may further combine inputs received from layers of the DUnet model to preserve signal amplitude trend.


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