Contaminant point source localization error estimates as functions of data quantity and model quality
Abstract
We develop empiricallygrounded error envelopes for localization of a point contamination release event in the saturated zone of a previously uncharacterized heterogeneous aquifer into which a number of plumeintercepting wells have been drilled. We assume that flow direction in the aquifer is known exactly and velocity is known to within a factor of two of our best guess from well observations prior to source identification. Other aquifer and source parameters must be estimated by interpretation of well breakthrough data via the advectiondispersion equation. We employ high performance computing to generate numerous random realizations of aquifer parameters and well locations, simulate well breakthrough data, and then employ unsupervised machine optimization techniques to estimate the most likely spatial (or spacetime) location of the source. Tabulating the accuracy of these estimates from the multiple realizations, we relate the size of 90% and 95% confidence envelopes to the data quantity (number of wells) and model quality (fidelity of ADE interpretation model to actual concentrations in a heterogeneous aquifer with channelized flow). We find that for purely spatial localization of the contaminant source, increased data quantities can make up for reduced model quality. For spacetime localization, we find similar qualitative behavior, but significantly degraded spatialmore »
 Authors:

 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Publication Date:
 Research Org.:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1329903
 Report Number(s):
 LAUR1622173
Journal ID: ISSN 01697722
 Grant/Contract Number:
 AC5206NA25396
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Journal of Contaminant Hydrology
 Additional Journal Information:
 Journal Volume: 193; Journal Issue: C; Journal ID: ISSN 01697722
 Publisher:
 Elsevier
 Country of Publication:
 United States
 Language:
 English
 Subject:
 54 ENVIRONMENTAL SCIENCES; Earth Sciences; Solute transport; Inverse problems; Environmental forensics; Source identification; Error quantification; Model error
Citation Formats
Hansen, Scott K., and Vesselinov, Velimir Valentinov. Contaminant point source localization error estimates as functions of data quantity and model quality. United States: N. p., 2016.
Web. https://doi.org/10.1016/j.jconhyd.2016.09.003.
Hansen, Scott K., & Vesselinov, Velimir Valentinov. Contaminant point source localization error estimates as functions of data quantity and model quality. United States. https://doi.org/10.1016/j.jconhyd.2016.09.003
Hansen, Scott K., and Vesselinov, Velimir Valentinov. Sat .
"Contaminant point source localization error estimates as functions of data quantity and model quality". United States. https://doi.org/10.1016/j.jconhyd.2016.09.003. https://www.osti.gov/servlets/purl/1329903.
@article{osti_1329903,
title = {Contaminant point source localization error estimates as functions of data quantity and model quality},
author = {Hansen, Scott K. and Vesselinov, Velimir Valentinov},
abstractNote = {We develop empiricallygrounded error envelopes for localization of a point contamination release event in the saturated zone of a previously uncharacterized heterogeneous aquifer into which a number of plumeintercepting wells have been drilled. We assume that flow direction in the aquifer is known exactly and velocity is known to within a factor of two of our best guess from well observations prior to source identification. Other aquifer and source parameters must be estimated by interpretation of well breakthrough data via the advectiondispersion equation. We employ high performance computing to generate numerous random realizations of aquifer parameters and well locations, simulate well breakthrough data, and then employ unsupervised machine optimization techniques to estimate the most likely spatial (or spacetime) location of the source. Tabulating the accuracy of these estimates from the multiple realizations, we relate the size of 90% and 95% confidence envelopes to the data quantity (number of wells) and model quality (fidelity of ADE interpretation model to actual concentrations in a heterogeneous aquifer with channelized flow). We find that for purely spatial localization of the contaminant source, increased data quantities can make up for reduced model quality. For spacetime localization, we find similar qualitative behavior, but significantly degraded spatial localization reliability and less improvement from extra data collection. Since the spacetime source localization problem is much more challenging, we also tried a multipleinitialguess optimization strategy. Furthermore, this greatly enhanced performance, but gains from additional data collection remained limited.},
doi = {10.1016/j.jconhyd.2016.09.003},
journal = {Journal of Contaminant Hydrology},
number = C,
volume = 193,
place = {United States},
year = {2016},
month = {10}
}
Web of Science
Works referencing / citing this record:
Contaminant source localization via Bayesian global optimization
journal, January 2019
 Pirot, Guillaume; Krityakierne, Tipaluck; Ginsbourger, David
 Hydrology and Earth System Sciences, Vol. 23, Issue 1