Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Univ. of California, Berkeley, CA (United States)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Fossil Energy (FE)
- OSTI Identifier:
- 1545299
- Alternate Identifier(s):
- OSTI ID: 1863165
- Report Number(s):
- LLNL-JRNL-833765
Journal ID: ISSN 0956-540X
- Grant/Contract Number:
- AC52-07NA27344
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Geophysical Journal International
- Additional Journal Information:
- Journal Volume: 215; Journal Issue: 3; Journal ID: ISSN 0956-540X
- Publisher:
- Oxford University Press
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING; Inverse Theory; Numerical Solutions; Computational Seismology; Neural Networks
Citation Formats
Lin, Youzuo, Wang, Shusen, Thiagarajan, Jayaraman, Guthrie, George, and Coblentz, David. Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm. United States: N. p., 2018.
Web. doi:10.1093/gji/ggy385.
Lin, Youzuo, Wang, Shusen, Thiagarajan, Jayaraman, Guthrie, George, & Coblentz, David. Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm. United States. https://doi.org/10.1093/gji/ggy385
Lin, Youzuo, Wang, Shusen, Thiagarajan, Jayaraman, Guthrie, George, and Coblentz, David. Tue .
"Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm". United States. https://doi.org/10.1093/gji/ggy385. https://www.osti.gov/servlets/purl/1545299.
@article{osti_1545299,
title = {Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm},
author = {Lin, Youzuo and Wang, Shusen and Thiagarajan, Jayaraman and Guthrie, George and Coblentz, David},
abstractNote = {},
doi = {10.1093/gji/ggy385},
journal = {Geophysical Journal International},
number = 3,
volume = 215,
place = {United States},
year = {Tue Sep 18 00:00:00 EDT 2018},
month = {Tue Sep 18 00:00:00 EDT 2018}
}
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Cited by: 4 works
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Figures / Tables:
Figure 1: The category of the different seismic exploration approaches. The data-driven methods can be categorized into either ‘learning from pre-stack data’ or ‘learning from migrated model’. The major difference between these two types of methods is whether a machine learning method works on the pre-stack seismic data sets ormore »
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Works referenced in this record:
A new optimization approach for source-encoding full-waveform inversion
journal, May 2013
- Moghaddam, Peyman P.; Keers, Henk; Herrmann, Felix J.
- GEOPHYSICS, Vol. 78, Issue 3
Hierarchically Compositional Kernels for Scalable Nonparametric Learning
preprint, January 2016
- Chen, Jie; Avron, Haim; Sindhwani, Vikas
- arXiv
Figures / Tables found in this record:
Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.