Methods and systems for obtaining reconstructed low-frequency seismic data for determining a subsurface feature
Abstract
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.
- Inventors:
- Issue Date:
- Research Org.:
- Advanced Geophysical Technology, Inc., Houston, TX (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1924968
- Patent Number(s):
- 11409011
- Application Number:
- 16/923,525
- Assignee:
- Advanced Geophysical Technology Inc. (Houston, TX)
- Patent Classifications (CPCs):
-
G - PHYSICS G01 - MEASURING G01V - GEOPHYSICS
- DOE Contract Number:
- SC0019665
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 07/08/2020
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING
Citation Formats
Hu, Wenyi. Methods and systems for obtaining reconstructed low-frequency seismic data for determining a subsurface feature. United States: N. p., 2022.
Web.
Hu, Wenyi. Methods and systems for obtaining reconstructed low-frequency seismic data for determining a subsurface feature. United States.
Hu, Wenyi. Tue .
"Methods and systems for obtaining reconstructed low-frequency seismic data for determining a subsurface feature". United States. https://www.osti.gov/servlets/purl/1924968.
@article{osti_1924968,
title = {Methods and systems for obtaining reconstructed low-frequency seismic data for determining a subsurface feature},
author = {Hu, Wenyi},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {8}
}
Works referenced in this record:
Generative Adversarial Network Seismic Data Processor
patent-application, October 2019
- Alwon, Stephen
- US Patent Application 16/364,330; 2019/0302290 Al
Machine Learning Training Set Generation
patent-application, June 2019
- Aqrawl, Ahmed Adnan; Salman, Nader; van der Hoff, Guido Johannes
- US Patent Application 16/324,109; 2019/0169962 Al
Machine Learning-Based Analysis of Seismic Attributes
patent-application, March 2020
- Roy, Atish; Kumar, Dhananjay; Kazlauskas, Eric
- US Patent Application 16/568,352; 2020/0088897 Al
Full-waveform inversion with the reconstructed wavefield method
conference, September 2016
- Wang, Chao; Yingst, David; Farmer, Paul
- SEG Technical Program Expanded Abstracts 2016
Deep-Learning Inversion of Seismic Data
journal, March 2020
- Li, Shucai; Liu, Bin; Ren, Yuxiao
- IEEE Transactions on Geoscience and Remote Sensing, Vol. 58, Issue 3