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:
Jun. 03, 2025

Filed:

May. 26, 2022
Applicant:

Landmark Graphics Corporation, Houston, TX (US);

Inventors:

Fan Jiang, Sugarland, TX (US);

Alejandro Jaramillo, Edinburgh, GB;

Steven Roy Angelovich, Livermore, CO (US);

Assignee:

Landmark Graphics Corporation, Houston, TX (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01V 1/34 (2006.01); G01V 1/28 (2006.01); G01V 1/30 (2006.01); G06N 20/20 (2019.01);
U.S. Cl.
CPC ...
G01V 1/345 (2013.01); G01V 1/282 (2013.01); G01V 1/301 (2013.01); G06N 20/20 (2019.01); G01V 2210/642 (2013.01);
Abstract

Frequency-dependent machine-learning (ML) models can be used to interpret seismic data. A system can apply spectral decomposition to pre-processed training data to generate frequency-dependent training data of two or more frequencies. The system can train two or more ML models using the frequency-dependent training data. Subsequent to training the two or more ML models, the system can apply the two or more ML models to seismic data to generate two or more subterranean feature probability maps. The system can perform an analysis of aleatoric uncertainty on the two or more subterranean feature probability maps to create an uncertainty map for aleatoric uncertainty. Additionally, the system can generate a filtered subterranean feature probability map based on the uncertainty map for aleatoric uncertainty.


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