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Title: Hierarchical Bayesian method for mapping biogeochemical hot spots using induced polarization imaging

Journal Article · · Water Resources Research
DOI:https://doi.org/10.1002/2015WR017763· OSTI ID:1439188
 [1];  [2];  [3];  [1];  [1];  [1];  [1]
  1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Earth Sciences Division
  2. Vienna Univ. of Technology (Austria). Dept. of Geodesy and Geoinformation
  3. Vienna Univ. of Technology (Austria). Dept. of Geodesy and Geoinformation; Univ. of Bonn (Germany). Steinmann Inst. and Dept. of Geophysics
+ Show Author Affiliations

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.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1439188
Alternate ID(s):
OSTI ID: 1402275
Journal Information:
Water Resources Research, Vol. 52, Issue 1; Related Information: © 2015. American Geophysical Union. All Rights Reserved.; ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 31 works
Citation information provided by
Web of Science

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Cited By (4)

On the Role of Stern‐ and Diffuse‐Layer Polarization Mechanisms in Porous Media journal June 2019
Snowmelt Induced Hydrologic Perturbations Drive Dynamic Microbiological and Geochemical Behaviors across a Shallow Riparian Aquifer journal May 2016
Metatranscriptomic Analysis Reveals Unexpectedly Diverse Microbial Metabolism in a Biogeochemical Hot Spot in an Alluvial Aquifer journal January 2017
Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods journal January 2017

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biogeochemical hot spots
induced polarization imaging
Bayesian hierarchical model