Ghent University Academic Bibliography

Advanced

Direct prediction of spatially and temporally varying physical properties from time-lapse electrical resistance data

Thomas Hermans UGent, Erasmus Oware and Jef Caers (2016) WATER RESOURCES RESEARCH. 52(9). p.7262-7283
abstract
Time-lapse applications of electrical methods have grown significantly over the last decade. However, the quantitative interpretation of tomograms in terms of physical properties, such as salinity, temperature or saturation, remains difficult. In many applications, geophysical models are transformed into hydrological models, but this transformation suffers from spatially and temporally varying resolution resulting from the regularization used by the deterministic inversion. In this study, we investigate a prediction-focused approach (PFA) to directly estimate subsurface physical properties with electrical resistance data, circumventing the need for classic tomographic inversions. First, we generate a prior set of resistance data and physical property forecast through hydrogeological and geophysical simulations mimicking the field experiment. We reduce the dimension of both the data and the forecast through principal component analysis in order to keep the most informative part of both sets in a reduced dimension space. Then, we apply canonical correlation analysis to explore the relationship between the data and the forecast in their reduced dimension space. If a linear relationship can be established, the posterior distribution of the forecast can be directly sampled using a Gaussian process regression where the field data scores are the conditioning data. In this paper, we demonstrate PFA for various physical property distributions. We also develop a framework to propagate the estimated noise level in the reduced dimension space. We validate the results by a Monte Carlo study on the posterior distribution and demonstrate that PFA yields accurate uncertainty for the cases studied.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
electrical resistance tomography, time-lapse, prediction-focused approach, direct forecast, inversion, MULTIPLE-POINT STATISTICS, RESISTIVITY TOMOGRAPHY, SUBSURFACE PROCESSES, INVERSE PROBLEMS, AQUIFER, SCALE, ERT, MODELS, FLOW, SIMULATION
journal title
WATER RESOURCES RESEARCH
Water Resour. Res.
volume
52
issue
9
pages
7262 - 7283
Web of Science type
Article
Web of Science id
000386977900032
JCR category
WATER RESOURCES
JCR impact factor
4.397 (2016)
JCR rank
4/88 (2016)
JCR quartile
1 (2016)
ISSN
0043-1397
DOI
10.1002/2016WR019126
language
English
UGent publication?
no
classification
A1
id
8539845
handle
http://hdl.handle.net/1854/LU-8539845
date created
2017-11-30 14:07:08
date last changed
2017-12-19 13:11:16
@article{8539845,
  abstract     = {Time-lapse applications of electrical methods have grown significantly over the last decade. However, the quantitative interpretation of tomograms in terms of physical properties, such as salinity, temperature or saturation, remains difficult. In many applications, geophysical models are transformed into hydrological models, but this transformation suffers from spatially and temporally varying resolution resulting from the regularization used by the deterministic inversion. In this study, we investigate a prediction-focused approach (PFA) to directly estimate subsurface physical properties with electrical resistance data, circumventing the need for classic tomographic inversions. First, we generate a prior set of resistance data and physical property forecast through hydrogeological and geophysical simulations mimicking the field experiment. We reduce the dimension of both the data and the forecast through principal component analysis in order to keep the most informative part of both sets in a reduced dimension space. Then, we apply canonical correlation analysis to explore the relationship between the data and the forecast in their reduced dimension space. If a linear relationship can be established, the posterior distribution of the forecast can be directly sampled using a Gaussian process regression where the field data scores are the conditioning data. In this paper, we demonstrate PFA for various physical property distributions. We also develop a framework to propagate the estimated noise level in the reduced dimension space. We validate the results by a Monte Carlo study on the posterior distribution and demonstrate that PFA yields accurate uncertainty for the cases studied.},
  author       = {Hermans, Thomas and Oware, Erasmus and Caers, Jef},
  issn         = {0043-1397},
  journal      = {WATER RESOURCES RESEARCH},
  keyword      = {electrical resistance tomography,time-lapse,prediction-focused approach,direct forecast,inversion,MULTIPLE-POINT STATISTICS,RESISTIVITY TOMOGRAPHY,SUBSURFACE PROCESSES,INVERSE PROBLEMS,AQUIFER,SCALE,ERT,MODELS,FLOW,SIMULATION},
  language     = {eng},
  number       = {9},
  pages        = {7262--7283},
  title        = {Direct prediction of spatially and temporally varying physical properties from time-lapse electrical resistance data},
  url          = {http://dx.doi.org/10.1002/2016WR019126},
  volume       = {52},
  year         = {2016},
}

Chicago
Hermans, Thomas, Erasmus Oware, and Jef Caers. 2016. “Direct Prediction of Spatially and Temporally Varying Physical Properties from Time-lapse Electrical Resistance Data.” Water Resources Research 52 (9): 7262–7283.
APA
Hermans, Thomas, Oware, E., & Caers, J. (2016). Direct prediction of spatially and temporally varying physical properties from time-lapse electrical resistance data. WATER RESOURCES RESEARCH, 52(9), 7262–7283.
Vancouver
1.
Hermans T, Oware E, Caers J. Direct prediction of spatially and temporally varying physical properties from time-lapse electrical resistance data. WATER RESOURCES RESEARCH. 2016;52(9):7262–83.
MLA
Hermans, Thomas, Erasmus Oware, and Jef Caers. “Direct Prediction of Spatially and Temporally Varying Physical Properties from Time-lapse Electrical Resistance Data.” WATER RESOURCES RESEARCH 52.9 (2016): 7262–7283. Print.