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  • Tso et al (author copy of accepted manuscript)

    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Contaminant Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Contaminant Hydrology, 234, 2020 DOI: 10.1016/j.jconhyd.2020.103679

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Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection. / Tso, C.-H.M.; Johnson, T.C.; Song, X. et al.
In: Journal of Contaminant Hydrology, Vol. 234, 103679, 01.10.2020.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Tso, C-HM, Johnson, TC, Song, X, Chen, X, Kuras, O, Wilkinson, P, Uhlemann, S, Chambers, J & Binley, A 2020, 'Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection', Journal of Contaminant Hydrology, vol. 234, 103679. https://doi.org/10.1016/j.jconhyd.2020.103679

APA

Tso, C.-HM., Johnson, T. C., Song, X., Chen, X., Kuras, O., Wilkinson, P., Uhlemann, S., Chambers, J., & Binley, A. (2020). Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection. Journal of Contaminant Hydrology, 234, Article 103679. https://doi.org/10.1016/j.jconhyd.2020.103679

Vancouver

Tso CHM, Johnson TC, Song X, Chen X, Kuras O, Wilkinson P et al. Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection. Journal of Contaminant Hydrology. 2020 Oct 1;234:103679. Epub 2020 Jul 5. doi: 10.1016/j.jconhyd.2020.103679

Author

Tso, C.-H.M. ; Johnson, T.C. ; Song, X. et al. / Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection. In: Journal of Contaminant Hydrology. 2020 ; Vol. 234.

Bibtex

@article{5081c63072424201b3a674e705fe023d,
title = "Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection",
abstract = "Time-lapse electrical resistivity tomography (ERT) measurements provide indirectobservations of hydrological processes in the Earth's shallow subsurface at high spatial and temporal resolution. ERT has been used in the past decades to detect leaks and monitor the evolution of associated contaminant plumes. Specifically, inverted resistivity images allow visualization of the dynamic changes in the structure of the plume. However, existing methods do not allow the direct estimation of leak parameters (e.g. leak rate, location, etc.) and their uncertainties. We propose an ensemble-based data assimilation framework that evaluates proposed hydrological models against observed time-lapse ERT measurements without directly inverting for the resistivities. Each proposed hydrological model is run through the parallel coupled hydro-geophysical simulation code PFLOTRAN-E4D to obtain simulated ERT measurements. The ensemble of model proposals is then updated using an iterative ensemble smoother. We demonstrate the proposed framework on synthetic and field ERT data from controlled tracer injection experiments. Our results show that the approach allows joint identification of contaminant source location, initial release time, and solute loading from the cross-borehole time-lapse ERT data, alongside with an assessment of uncertainties in these estimates. We demonstrate a reduction in site-wide uncertainty by comparing the prior and posterior plume mass discharges at a selected image plane. This framework is particularly attractive to sites that have previously undergone extensive geological investigation (e.g., nuclear sites). It is well suited to complement ERT imaging and we discuss practical issues in its application to field problems. {\textcopyright} 2020 Elsevier B.V.",
author = "C.-H.M. Tso and T.C. Johnson and X. Song and X. Chen and O. Kuras and P. Wilkinson and S. Uhlemann and J. Chambers and A. Binley",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Journal of Contaminant Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Contaminant Hydrology, 234, 2020 DOI: 10.1016/j.jconhyd.2020.103679",
year = "2020",
month = oct,
day = "1",
doi = "10.1016/j.jconhyd.2020.103679",
language = "English",
volume = "234",
journal = "Journal of Contaminant Hydrology",
issn = "0169-7722",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection

AU - Tso, C.-H.M.

AU - Johnson, T.C.

AU - Song, X.

AU - Chen, X.

AU - Kuras, O.

AU - Wilkinson, P.

AU - Uhlemann, S.

AU - Chambers, J.

AU - Binley, A.

N1 - This is the author’s version of a work that was accepted for publication in Journal of Contaminant Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Contaminant Hydrology, 234, 2020 DOI: 10.1016/j.jconhyd.2020.103679

PY - 2020/10/1

Y1 - 2020/10/1

N2 - Time-lapse electrical resistivity tomography (ERT) measurements provide indirectobservations of hydrological processes in the Earth's shallow subsurface at high spatial and temporal resolution. ERT has been used in the past decades to detect leaks and monitor the evolution of associated contaminant plumes. Specifically, inverted resistivity images allow visualization of the dynamic changes in the structure of the plume. However, existing methods do not allow the direct estimation of leak parameters (e.g. leak rate, location, etc.) and their uncertainties. We propose an ensemble-based data assimilation framework that evaluates proposed hydrological models against observed time-lapse ERT measurements without directly inverting for the resistivities. Each proposed hydrological model is run through the parallel coupled hydro-geophysical simulation code PFLOTRAN-E4D to obtain simulated ERT measurements. The ensemble of model proposals is then updated using an iterative ensemble smoother. We demonstrate the proposed framework on synthetic and field ERT data from controlled tracer injection experiments. Our results show that the approach allows joint identification of contaminant source location, initial release time, and solute loading from the cross-borehole time-lapse ERT data, alongside with an assessment of uncertainties in these estimates. We demonstrate a reduction in site-wide uncertainty by comparing the prior and posterior plume mass discharges at a selected image plane. This framework is particularly attractive to sites that have previously undergone extensive geological investigation (e.g., nuclear sites). It is well suited to complement ERT imaging and we discuss practical issues in its application to field problems. © 2020 Elsevier B.V.

AB - Time-lapse electrical resistivity tomography (ERT) measurements provide indirectobservations of hydrological processes in the Earth's shallow subsurface at high spatial and temporal resolution. ERT has been used in the past decades to detect leaks and monitor the evolution of associated contaminant plumes. Specifically, inverted resistivity images allow visualization of the dynamic changes in the structure of the plume. However, existing methods do not allow the direct estimation of leak parameters (e.g. leak rate, location, etc.) and their uncertainties. We propose an ensemble-based data assimilation framework that evaluates proposed hydrological models against observed time-lapse ERT measurements without directly inverting for the resistivities. Each proposed hydrological model is run through the parallel coupled hydro-geophysical simulation code PFLOTRAN-E4D to obtain simulated ERT measurements. The ensemble of model proposals is then updated using an iterative ensemble smoother. We demonstrate the proposed framework on synthetic and field ERT data from controlled tracer injection experiments. Our results show that the approach allows joint identification of contaminant source location, initial release time, and solute loading from the cross-borehole time-lapse ERT data, alongside with an assessment of uncertainties in these estimates. We demonstrate a reduction in site-wide uncertainty by comparing the prior and posterior plume mass discharges at a selected image plane. This framework is particularly attractive to sites that have previously undergone extensive geological investigation (e.g., nuclear sites). It is well suited to complement ERT imaging and we discuss practical issues in its application to field problems. © 2020 Elsevier B.V.

U2 - 10.1016/j.jconhyd.2020.103679

DO - 10.1016/j.jconhyd.2020.103679

M3 - Journal article

VL - 234

JO - Journal of Contaminant Hydrology

JF - Journal of Contaminant Hydrology

SN - 0169-7722

M1 - 103679

ER -