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Title: Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection

Abstract

Time-lapse electrical resistivity tomography (ERT) measurements provide indirect observations 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. In this paper, 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 plumemore » 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.« less

Authors:
 [1]; ORCiD logo [2];  [2];  [2];  [3];  [3];  [4];  [3];  [1]
  1. Lancaster Univ., Bailrigg (United Kingdom)
  2. Battelle Pacific Northwest Labs., Richland, WA (United States)
  3. British Geological Survey, Keyworth (United Kingdom)
  4. British Geological Survey, Keyworth (United Kingdom); Battelle Pacific Northwest Labs., Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1650365
Report Number(s):
PNNL-SA-152552
Journal ID: ISSN 0169-7722
Grant/Contract Number:  
AC05-76RL01830; AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Contaminant Hydrology
Additional Journal Information:
Journal Volume: 234; Journal ID: ISSN 0169-7722
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES

Citation Formats

Tso, Chak-Hau Michael, Johnson, Timothy C., Song, Xuehang, Chen, Xingyuan, Kuras, Oliver, Wilkinson, Paul, Uhlemann, Sebastian, Chambers, Jonathan, and Binley, Andrew. Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection. United States: N. p., 2020. Web. doi:10.1016/j.jconhyd.2020.103679.
Tso, Chak-Hau Michael, Johnson, Timothy C., Song, Xuehang, Chen, Xingyuan, Kuras, Oliver, Wilkinson, Paul, Uhlemann, Sebastian, Chambers, Jonathan, & Binley, Andrew. Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection. United States. https://doi.org/10.1016/j.jconhyd.2020.103679
Tso, Chak-Hau Michael, Johnson, Timothy C., Song, Xuehang, Chen, Xingyuan, Kuras, Oliver, Wilkinson, Paul, Uhlemann, Sebastian, Chambers, Jonathan, and Binley, Andrew. 2020. "Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection". United States. https://doi.org/10.1016/j.jconhyd.2020.103679. https://www.osti.gov/servlets/purl/1650365.
@article{osti_1650365,
title = {Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection},
author = {Tso, Chak-Hau Michael and Johnson, Timothy C. and Song, Xuehang and Chen, Xingyuan and Kuras, Oliver and Wilkinson, Paul and Uhlemann, Sebastian and Chambers, Jonathan and Binley, Andrew},
abstractNote = {Time-lapse electrical resistivity tomography (ERT) measurements provide indirect observations 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. In this paper, 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.},
doi = {10.1016/j.jconhyd.2020.103679},
url = {https://www.osti.gov/biblio/1650365}, journal = {Journal of Contaminant Hydrology},
issn = {0169-7722},
number = ,
volume = 234,
place = {United States},
year = {Sun Jul 05 00:00:00 EDT 2020},
month = {Sun Jul 05 00:00:00 EDT 2020}
}

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