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

Abstract

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.more » 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.« less

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 Lab. (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
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. doi: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. doi: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 = {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.},
doi = {10.1093/gji/ggy385},
journal = {Geophysical Journal International},
number = 3,
volume = 215,
place = {United States},
year = {2018},
month = {9}
}

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Cited by: 1 work
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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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