Cargando…
Contaminant release history identification in 2-D heterogeneous aquifers through a minimum relative entropy approach
The minimum relative entropy (MRE) method has been applied in a wide variety of fields since it was first introduced. Woodbury and Ulrych (Water Resour Res 29(8): 2847–2860, 1993, Water Resour Res 32(9): 2671–2681, 1996) adopted and improved this method to solve linear inverse problems in aquifers....
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4628610/ https://www.ncbi.nlm.nih.gov/pubmed/26543790 http://dx.doi.org/10.1186/s40064-015-1465-x |
_version_ | 1782398457102729216 |
---|---|
author | Cupola, Fausto Tanda, Maria Giovanna Zanini, Andrea |
author_facet | Cupola, Fausto Tanda, Maria Giovanna Zanini, Andrea |
author_sort | Cupola, Fausto |
collection | PubMed |
description | The minimum relative entropy (MRE) method has been applied in a wide variety of fields since it was first introduced. Woodbury and Ulrych (Water Resour Res 29(8): 2847–2860, 1993, Water Resour Res 32(9): 2671–2681, 1996) adopted and improved this method to solve linear inverse problems in aquifers. In this work, the MRE method was improved to detect the source release history in 2-D aquifer characterized by a non-uniform flow-field. The approach was tested on two cases: a 2-D homogeneous conductivity field and a heterogeneous one (the hydraulic conductivity presents three orders of magnitude in terms of variability). In the latter case the transfer function cannot be described with an analytical formulation, thus, the transfer functions were estimated by means of a numerical procedure. In order to analyze the method performance in different conditions, two datasets have been used: observations collected at the same time at 20 different monitoring points, and observations collected at 2 monitoring points at several times. The observed data have been processed with and without a random error and the Boxcar and Gaussian probability distribution functions were considered as a priori information. The agreement between the true and the estimated data has been evaluated through the calculation of the normalized Root Mean Square error. The approach was able to recover the release history even in the most difficult case. |
format | Online Article Text |
id | pubmed-4628610 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-46286102015-11-05 Contaminant release history identification in 2-D heterogeneous aquifers through a minimum relative entropy approach Cupola, Fausto Tanda, Maria Giovanna Zanini, Andrea Springerplus Research The minimum relative entropy (MRE) method has been applied in a wide variety of fields since it was first introduced. Woodbury and Ulrych (Water Resour Res 29(8): 2847–2860, 1993, Water Resour Res 32(9): 2671–2681, 1996) adopted and improved this method to solve linear inverse problems in aquifers. In this work, the MRE method was improved to detect the source release history in 2-D aquifer characterized by a non-uniform flow-field. The approach was tested on two cases: a 2-D homogeneous conductivity field and a heterogeneous one (the hydraulic conductivity presents three orders of magnitude in terms of variability). In the latter case the transfer function cannot be described with an analytical formulation, thus, the transfer functions were estimated by means of a numerical procedure. In order to analyze the method performance in different conditions, two datasets have been used: observations collected at the same time at 20 different monitoring points, and observations collected at 2 monitoring points at several times. The observed data have been processed with and without a random error and the Boxcar and Gaussian probability distribution functions were considered as a priori information. The agreement between the true and the estimated data has been evaluated through the calculation of the normalized Root Mean Square error. The approach was able to recover the release history even in the most difficult case. Springer International Publishing 2015-10-31 /pmc/articles/PMC4628610/ /pubmed/26543790 http://dx.doi.org/10.1186/s40064-015-1465-x Text en © Cupola et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Cupola, Fausto Tanda, Maria Giovanna Zanini, Andrea Contaminant release history identification in 2-D heterogeneous aquifers through a minimum relative entropy approach |
title | Contaminant release history identification in 2-D heterogeneous aquifers through a minimum relative entropy approach |
title_full | Contaminant release history identification in 2-D heterogeneous aquifers through a minimum relative entropy approach |
title_fullStr | Contaminant release history identification in 2-D heterogeneous aquifers through a minimum relative entropy approach |
title_full_unstemmed | Contaminant release history identification in 2-D heterogeneous aquifers through a minimum relative entropy approach |
title_short | Contaminant release history identification in 2-D heterogeneous aquifers through a minimum relative entropy approach |
title_sort | contaminant release history identification in 2-d heterogeneous aquifers through a minimum relative entropy approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4628610/ https://www.ncbi.nlm.nih.gov/pubmed/26543790 http://dx.doi.org/10.1186/s40064-015-1465-x |
work_keys_str_mv | AT cupolafausto contaminantreleasehistoryidentificationin2dheterogeneousaquifersthroughaminimumrelativeentropyapproach AT tandamariagiovanna contaminantreleasehistoryidentificationin2dheterogeneousaquifersthroughaminimumrelativeentropyapproach AT zaniniandrea contaminantreleasehistoryidentificationin2dheterogeneousaquifersthroughaminimumrelativeentropyapproach |