We develop empirically-grounded 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 plume-intercepting 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 advection-dispersion 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 space-time) 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 space-time localization, we find similar qualitative behavior, but significantly degraded spatialmore »

- Publication Date:

- Report Number(s):
- LA-UR-16-22173

Journal ID: ISSN 0169-7722

- Grant/Contract Number:
- AC52-06NA25396

- Type:
- Accepted Manuscript

- Journal Name:
- Journal of Contaminant Hydrology

- Additional Journal Information:
- Journal Volume: 193; Journal Issue: C; Journal ID: ISSN 0169-7722

- Publisher:
- Elsevier

- Research Org:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

- Sponsoring Org:
- USDOE

- 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

- OSTI Identifier:
- 1329903

```
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.,
Web. doi: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. doi:10.1016/j.jconhyd.2016.09.003.

```
Hansen, Scott K., and Vesselinov, Velimir Valentinov. 2016.
"Contaminant point source localization error estimates as functions of data quantity and model quality". United States.
doi: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 empirically-grounded 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 plume-intercepting 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 advection-dispersion 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 space-time) 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 space-time localization, we find similar qualitative behavior, but significantly degraded spatial localization reliability and less improvement from extra data collection. Since the space-time source localization problem is much more challenging, we also tried a multiple-initial-guess 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}

}