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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 10
Hydrol. Earth Syst. Sci., 14, 1989–2001, 2010
https://doi.org/10.5194/hess-14-1989-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 14, 1989–2001, 2010
https://doi.org/10.5194/hess-14-1989-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Oct 2010

21 Oct 2010

Bayesian approach for three-dimensional aquifer characterization at the Hanford 300 Area

H. Murakami1, X. Chen2, M. S. Hahn2, Y. Liu2, M. L. Rockhold3, V. R. Vermeul3, J. M. Zachara3, and Y. Rubin2 H. Murakami et al.
  • 1Department of Nuclear Engineering, University of California, Berkeley, California, USA
  • 2Department of Civil and Environmental Engineering, University of California, Berkeley, California, USA
  • 3Pacific Northwest National Laboratory, Richland, WA, USA

Abstract. This study presents a stochastic, three-dimensional characterization of a heterogeneous hydraulic conductivity field within the Hanford 300 Area, Washington, USA, by assimilating large-scale, constant-rate injection test data with small-scale, three-dimensional electromagnetic borehole flowmeter (EBF) measurement data. We first inverted the injection test data to estimate the transmissivity field, using zeroth-order temporal moments of pressure buildup curves. We applied a newly developed Bayesian geostatistical inversion framework, the method of anchored distributions (MAD), to obtain a joint posterior distribution of geostatistical parameters and local log-transmissivities at multiple locations. The unique aspects of MAD that make it suitable for this purpose are its ability to integrate multi-scale, multi-type data within a Bayesian framework and to compute a nonparametric posterior distribution. After we combined the distribution of transmissivities with depth-discrete relative-conductivity profile from the EBF data, we inferred the three-dimensional geostatistical parameters of the log-conductivity field, using the Bayesian model-based geostatistics. Such consistent use of the Bayesian approach throughout the procedure enabled us to systematically incorporate data uncertainty into the final posterior distribution. The method was tested in a synthetic study and validated using the actual data that was not part of the estimation. Results showed broader and skewed posterior distributions of geostatistical parameters except for the mean, which suggests the importance of inferring the entire distribution to quantify the parameter uncertainty.

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