skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging: SCALABLE INVERSE MODELING WITH A HUGE MRI DATA SET

Authors:
 [1];  [2];  [1];  [3];  [4]
  1. Department of Civil and Environmental Engineering, Stanford University, Stanford California USA
  2. Geoscience Research and Applications Group, Sandia National Laboratories, Albuquerque New Mexico USA
  3. Department of Civil and Environmental Engineering, University of Texas, Austin Texas USA
  4. Department of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign Illinois USA
Publication Date:
Research Org.:
Energy Frontier Research Centers (EFRC) (United States). Center for Frontiers of Subsurface Energy Security (CFSES)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1388714
DOE Contract Number:  
SC0001114
Resource Type:
Journal Article
Resource Relation:
Journal Name: Water Resources Research; Journal Volume: 52; Journal Issue: 7; Related Information: CFSES partners with University of Texas at Austin (lead); Sandia National Laboratory
Country of Publication:
United States
Language:
English
Subject:
nuclear (including radiation effects), carbon sequestration

Citation Formats

Lee, Jonghyun, Yoon, Hongkyu, Kitanidis, Peter K., Werth, Charles J., and Valocchi, Albert J.. Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging: SCALABLE INVERSE MODELING WITH A HUGE MRI DATA SET. United States: N. p., 2016. Web. doi:10.1002/2015WR018483.
Lee, Jonghyun, Yoon, Hongkyu, Kitanidis, Peter K., Werth, Charles J., & Valocchi, Albert J.. Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging: SCALABLE INVERSE MODELING WITH A HUGE MRI DATA SET. United States. doi:10.1002/2015WR018483.
Lee, Jonghyun, Yoon, Hongkyu, Kitanidis, Peter K., Werth, Charles J., and Valocchi, Albert J.. Fri . "Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging: SCALABLE INVERSE MODELING WITH A HUGE MRI DATA SET". United States. doi:10.1002/2015WR018483.
@article{osti_1388714,
title = {Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging: SCALABLE INVERSE MODELING WITH A HUGE MRI DATA SET},
author = {Lee, Jonghyun and Yoon, Hongkyu and Kitanidis, Peter K. and Werth, Charles J. and Valocchi, Albert J.},
abstractNote = {},
doi = {10.1002/2015WR018483},
journal = {Water Resources Research},
number = 7,
volume = 52,
place = {United States},
year = {Fri Jul 01 00:00:00 EDT 2016},
month = {Fri Jul 01 00:00:00 EDT 2016}
}