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Title: Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm

Authors:
 [1];  [2];  [3];  [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of California, Berkeley, CA (United States)
  3. 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}
}

Journal Article:
Free Publicly Available Full Text
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Cited by: 4 works
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Figures / Tables:

Figure 1 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 » migrated/inverted models.« less

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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
  • DOI: 10.1190/geo2012-0090.1

Hierarchically Compositional Kernels for Scalable Nonparametric Learning
preprint, January 2016