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Title: Efficient gHMC Reconstruction of Contaminant Release History

Abstract

We present a generalized hybrid Monte Carlo (gHMC) method for fast, statistically optimal reconstruction of release histories of reactive contaminants. The approach is applicable to large-scale, strongly nonlinear systems with parametric uncertainties and data corrupted by measurement errors. The use of discrete adjoint equations facilitates numerical implementation of gHMC, without putting any restrictions on the degree of nonlinearity of advection-dispersion-reaction equations that are used to describe contaminant transport in the subsurface. To demonstrate the salient features of the proposed algorithm, we identify the spatial extent of a distributed source of contamination from concentration measurements of a reactive solute.

Authors:
ORCiD logo [1];  [2];  [3];  [4]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Computational Mathematics
  2. Brookhaven National Lab. (BNL), Upton, NY (United States). Computational Science Initiative
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  4. Stanford Univ., CA (United States). Dept. of Energy Resources Engineering
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Stanford Univ., CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); National Science Foundation (NSF)
OSTI Identifier:
1570043
Alternate Identifier(s):
OSTI ID: 1574394; OSTI ID: 1574861
Report Number(s):
PNNL-SA-147257; DOE-STANFORD-0019130-2
Journal ID: ISSN 2296-665X
Grant/Contract Number:  
AC05-76RL01830; SC0019393; SC0019130
Resource Type:
Published Article
Journal Name:
Frontiers in Environmental Science
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 2296-665X
Publisher:
Frontiers Research Foundation
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; source identification; contaminant transport; hybrid monte carlo; inverse problems; uncertainty quantification; Markov chain Monte Carlo

Citation Formats

Barajas-Solano, David A., Alexander, Francis J., Anghel, Marian, and Tartakovsky, Daniel M. Efficient gHMC Reconstruction of Contaminant Release History. United States: N. p., 2019. Web. doi:10.3389/fenvs.2019.00149.
Barajas-Solano, David A., Alexander, Francis J., Anghel, Marian, & Tartakovsky, Daniel M. Efficient gHMC Reconstruction of Contaminant Release History. United States. doi:10.3389/fenvs.2019.00149.
Barajas-Solano, David A., Alexander, Francis J., Anghel, Marian, and Tartakovsky, Daniel M. Fri . "Efficient gHMC Reconstruction of Contaminant Release History". United States. doi:10.3389/fenvs.2019.00149.
@article{osti_1570043,
title = {Efficient gHMC Reconstruction of Contaminant Release History},
author = {Barajas-Solano, David A. and Alexander, Francis J. and Anghel, Marian and Tartakovsky, Daniel M.},
abstractNote = {We present a generalized hybrid Monte Carlo (gHMC) method for fast, statistically optimal reconstruction of release histories of reactive contaminants. The approach is applicable to large-scale, strongly nonlinear systems with parametric uncertainties and data corrupted by measurement errors. The use of discrete adjoint equations facilitates numerical implementation of gHMC, without putting any restrictions on the degree of nonlinearity of advection-dispersion-reaction equations that are used to describe contaminant transport in the subsurface. To demonstrate the salient features of the proposed algorithm, we identify the spatial extent of a distributed source of contamination from concentration measurements of a reactive solute.},
doi = {10.3389/fenvs.2019.00149},
journal = {Frontiers in Environmental Science},
number = ,
volume = 7,
place = {United States},
year = {2019},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.3389/fenvs.2019.00149

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