Abstract
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.
Highlights
Hydrogeological characterization plays a key role in various projects involving groundwater flow and contaminant transport
We first tested the method of anchored distributions (MAD) and numerical setting in a synthetic study for inverting the injection test data, and we applied it to the actual data at the Hanford site
Our inversion process is based on the same sets of injection and observation wells as the actual experiments conducted at the Integrated Field Research Challenge (IFRC) site (Fig. 2)
Summary
Hydrogeological characterization plays a key role in various projects involving groundwater flow and contaminant transport. Recent focus on geochemical and microbiological reactions in field studies, for example, requires flow parameters to be fully characterized a priori for testing their research hypotheses (Scheibe et al, 2001; Scheibe and Chien, 2003; Fienen et al, 2004). With stochastic modeling of flow and transport becoming increasingly common, it is important to combine best-fitted values from each dataset, and to correctly quantify and weigh errors and uncertainty associated with different datasets, and to transfer the uncertainty to the final prediction (Maxwell et al, 1999; Hou and Rubin, 2005; De Barros et al, 2009)
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