US 12,320,939 B2
Frequency-dependent machine learning model in seismic interpretation
Fan Jiang, Sugarland, TX (US); Alejandro Jaramillo, Edinburgh (GB); and Steven Roy Angelovich, Livermore, CO (US)
Assigned to Landmark Graphics Corporation, Houston, TX (US)
Filed by Landmark Graphics Corporation, Houston, TX (US)
Filed on May 26, 2022, as Appl. No. 17/825,914.
Claims priority of provisional application 63/317,825, filed on Mar. 8, 2022.
Prior Publication US 2023/0288594 A1, Sep. 14, 2023
Int. Cl. G01V 1/34 (2006.01); G01V 1/28 (2006.01); G01V 1/30 (2006.01); G06N 20/20 (2019.01)
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)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
applying spectral decomposition to pre-processed training data to generate frequency-dependent training data of two or more frequencies;
training two or more machine-learning (ML) models using the frequency-dependent training data, wherein each ML model of the two or more ML models comprises a plurality of layers, wherein each ML model of the two or more ML models is trained using frequency-dependent training data of a different frequency than a frequency of frequency-dependent training data that is used to train a different ML model of the two or more ML models;
subsequent to training the two or more ML models, applying the two or more ML models to seismic data to generate two or more subterranean feature probability maps;
performing an analysis of aleatoric uncertainty on the two or more subterranean feature probability maps to create an uncertainty map for aleatoric uncertainty; and
generating a filtered subterranean feature probability map based on the uncertainty map for aleatoric uncertainty.