Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging-Reference-Cited by-同舟云学术

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Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging

Published:2016-07 Issue:7 Volume:52 Page:5213-5231
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Lee Jonghyun¹,Yoon Hongkyu²,Kitanidis Peter K.¹,Werth Charles J.³,Valocchi Albert J.⁴

Affiliation:

1. Department of Civil and Environmental EngineeringStanford UniversityStanford California USA

2. Geoscience Research and Applications Group, Sandia National LaboratoriesAlbuquerque New Mexico USA

3. Department of Civil and Environmental EngineeringUniversity of TexasAustin Texas USA

4. Department of Civil and Environmental EngineeringUniversity of IllinoisUrbana‐Champaign Illinois USA

Funder

National Science Foundation

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/2015WR018483

Reference52 articles.

1. Robust inversion, dimensionality reduction, and randomized sampling

2. On Integrating Groundwater Transport Models with Wireless Sensor Networks

3. Numerical solution of saddle point problems

4. Hybrid Krylov Methods for Nonlinear Systems of Equations

5. Inverse problem in hydrogeology

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