skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Methods and systems for obtaining reconstructed low-frequency seismic data for determining a subsurface feature

Patent ·
OSTI ID:1924968

A computer-implemented method for obtaining reconstructed seismic data for determining a subsurface feature, includes: determining an initial training velocity model, training a machine learning model based on first training seismic data and second training seismic data generated from the training velocity model, the first training seismic data corresponding to one or more first frequencies, the second training seismic data corresponding to one or more second frequencies lower than the one or more first frequencies, obtaining, based on measured seismic data and the machine learning model, reconstructed seismic data corresponding to the one or more second frequencies, generating a velocity model based on the measured seismic data, the reconstructed seismic data, and a full waveform inversion (FWI), and when the generated velocity model does not satisfy a preset condition, updating the training velocity model based on the generated velocity model, to obtain updated reconstructed seismic data for determining a subsurface feature.

Research Organization:
Advanced Geophysical Technology, Inc., Houston, TX (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
SC0019665
Assignee:
Advanced Geophysical Technology Inc. (Houston, TX)
Patent Number(s):
11,409,011
Application Number:
16/923,525
OSTI ID:
1924968
Resource Relation:
Patent File Date: 07/08/2020
Country of Publication:
United States
Language:
English

References (5)

Generative Adversarial Network Seismic Data Processor patent-application October 2019
Machine Learning Training Set Generation patent-application June 2019
Machine Learning-Based Analysis of Seismic Attributes patent-application March 2020
Full-waveform inversion with the reconstructed wavefield method conference September 2016
Deep-Learning Inversion of Seismic Data journal March 2020