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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)
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Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency and subjective human factors. We developed a novel data-driven geological feature characterization approach based on pre-stack seismic measurements. Our characterization method employs an efficient and accurate machine learning method to extract useful subsurface geologic features automatically. Specifically, we use kernel ridge regression to account for the nonlinear relationship between seismic data and geological features. We moreover employ kernel tricks to avoid the explicit nonlinear mapping and infinite dimension of feature space. Yet, the conventional kernel ridge regression can be computationally prohibitive because of the large volume of seismic measurements. We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage. In particular, we utilize a randomized numerical linear algebra technique, named Nyström method, to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate characterization. We provide thorough computational cost analysis to show the efficiency of our new geological feature characterization methods. We validate the performance of our method in characterizing geologic fault zones because faults play an important role in various subsurface applications. Our numerical examples demonstrate that our new characterization method significantly improves the computational efficiency while maintaining comparable accuracy. Interestingly, we demonstrate that our approach yields a speedup ratio on the order of ~102 to ~103 in a multicore computational environment.

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 (3)

Approximating a Gram Matrix for Improved Kernel-Based Learning book January 2005
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)

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58 GEOSCIENCES
97 MATHEMATICS AND COMPUTING
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