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

Journal Article · · Geophysical Journal International
DOI:https://doi.org/10.1093/gji/ggy385· OSTI ID:1545299
 [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)
+ Show Author Affiliations

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Fossil Energy (FE)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1545299
Alternate ID(s):
OSTI ID: 1863165
Report Number(s):
LLNL-JRNL-833765
Journal Information:
Geophysical Journal International, Vol. 215, Issue 3; ISSN 0956-540X
Publisher:
Oxford University PressCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 4 works
Citation information provided by
Web of Science

References (2)

A new optimization approach for source-encoding full-waveform inversion journal May 2013
Hierarchically Compositional Kernels for Scalable Nonparametric Learning preprint January 2016

Figures / Tables (13)

Figure 1(p. 2)figure Figure 1
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Figure 10(p. 11)figure Figure 10
Figure 11(p. 12)figure Figure 11
Figure 12(p. 12)figure Figure 12
Figure 13(p. 13)figure Figure 13

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Related Subjects

58 GEOSCIENCES
97 MATHEMATICS AND COMPUTING
Inverse Theory
Numerical Solutions
Computational Seismology
Neural Networks