Hierarchical Bayesian method for mapping biogeochemical hot spots using induced polarization imaging
Abstract
In floodplain environments, a naturally reduced zone (NRZ) is considered to be a common biogeochemical hot spot, having distinct microbial and geochemical characteristics. Although important for understanding their role in mediating floodplain biogeochemical processes, mapping the subsurface distribution of NRZs over the dimensions of a floodplain is challenging, as conventional wellbore data are typically spatially limited and the distribution of NRZs is heterogeneous. In this work, we present an innovative methodology for the probabilistic mapping of NRZs within a three-dimensional (3-D) subsurface domain using induced polarization imaging, which is a noninvasive geophysical technique. Measurements consist of surface geophysical surveys and drilling-recovered sediments at the U.S. Department of Energy field site near Rifle, CO (USA). Inversion of surface time domain-induced polarization (TDIP) data yielded 3-D images of the complex electrical resistivity, in terms of magnitude and phase, which are associated with mineral precipitation and other lithological properties. By extracting the TDIP data values colocated with wellbore lithological logs, we found that the NRZs have a different distribution of resistivity and polarization from the other aquifer sediments. To estimate the spatial distribution of NRZs, we developed a Bayesian hierarchical model to integrate the geophysical and wellbore data. In addition, the resistivity imagesmore »
- Authors:
-
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Earth Sciences Division
- Vienna Univ. of Technology (Austria). Dept. of Geodesy and Geoinformation
- Vienna Univ. of Technology (Austria). Dept. of Geodesy and Geoinformation; Univ. of Bonn (Germany). Steinmann Inst. and Dept. of Geophysics
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1439188
- Alternate Identifier(s):
- OSTI ID: 1402275
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Water Resources Research
- Additional Journal Information:
- Journal Volume: 52; Journal Issue: 1; Related Information: © 2015. American Geophysical Union. All Rights Reserved.; Journal ID: ISSN 0043-1397
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 47 OTHER INSTRUMENTATION; biogeochemical hot spots; induced polarization imaging; Bayesian hierarchical model
Citation Formats
Wainwright, Haruko M., Flores Orozco, Adrian, Bucker, Matthias, Dafflon, Baptiste, Chen, Jinsong, Hubbard, Susan S., and Williams, Kenneth H. Hierarchical Bayesian method for mapping biogeochemical hot spots using induced polarization imaging. United States: N. p., 2016.
Web. doi:10.1002/2015WR017763.
Wainwright, Haruko M., Flores Orozco, Adrian, Bucker, Matthias, Dafflon, Baptiste, Chen, Jinsong, Hubbard, Susan S., & Williams, Kenneth H. Hierarchical Bayesian method for mapping biogeochemical hot spots using induced polarization imaging. United States. https://doi.org/10.1002/2015WR017763
Wainwright, Haruko M., Flores Orozco, Adrian, Bucker, Matthias, Dafflon, Baptiste, Chen, Jinsong, Hubbard, Susan S., and Williams, Kenneth H. Fri .
"Hierarchical Bayesian method for mapping biogeochemical hot spots using induced polarization imaging". United States. https://doi.org/10.1002/2015WR017763. https://www.osti.gov/servlets/purl/1439188.
@article{osti_1439188,
title = {Hierarchical Bayesian method for mapping biogeochemical hot spots using induced polarization imaging},
author = {Wainwright, Haruko M. and Flores Orozco, Adrian and Bucker, Matthias and Dafflon, Baptiste and Chen, Jinsong and Hubbard, Susan S. and Williams, Kenneth H.},
abstractNote = {In floodplain environments, a naturally reduced zone (NRZ) is considered to be a common biogeochemical hot spot, having distinct microbial and geochemical characteristics. Although important for understanding their role in mediating floodplain biogeochemical processes, mapping the subsurface distribution of NRZs over the dimensions of a floodplain is challenging, as conventional wellbore data are typically spatially limited and the distribution of NRZs is heterogeneous. In this work, we present an innovative methodology for the probabilistic mapping of NRZs within a three-dimensional (3-D) subsurface domain using induced polarization imaging, which is a noninvasive geophysical technique. Measurements consist of surface geophysical surveys and drilling-recovered sediments at the U.S. Department of Energy field site near Rifle, CO (USA). Inversion of surface time domain-induced polarization (TDIP) data yielded 3-D images of the complex electrical resistivity, in terms of magnitude and phase, which are associated with mineral precipitation and other lithological properties. By extracting the TDIP data values colocated with wellbore lithological logs, we found that the NRZs have a different distribution of resistivity and polarization from the other aquifer sediments. To estimate the spatial distribution of NRZs, we developed a Bayesian hierarchical model to integrate the geophysical and wellbore data. In addition, the resistivity images were used to estimate hydrostratigraphic interfaces under the floodplain. Validation results showed that the integration of electrical imaging and wellbore data using a Bayesian hierarchical model was capable of mapping spatially heterogeneous interfaces and NRZ distributions thereby providing a minimally invasive means to parameterize a hydrobiogeochemical model of the floodplain.},
doi = {10.1002/2015WR017763},
journal = {Water Resources Research},
number = 1,
volume = 52,
place = {United States},
year = {Fri Jan 29 00:00:00 EST 2016},
month = {Fri Jan 29 00:00:00 EST 2016}
}
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