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Estimating the spatiotemporal distribution of geochemical parameters associated with biostimulation using spectral induced polarization data and hierarchical Bayesian models
Author(s) -
Chen Jinsong,
Hubbard Susan S.,
Williams Kenneth H.,
Flores Orozco Adrián,
Kemna Andreas
Publication year - 2012
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2011wr010992
Subject(s) - markov chain monte carlo , soil science , aquifer , environmental science , monte carlo method , groundwater , geology , statistics , mathematics , geotechnical engineering
We developed a hierarchical Bayesian model to estimate the spatiotemporal distribution of aqueous geochemical parameters associated with in‐situ bioremediation using surface spectral induced polarization (SIP) data and borehole geochemical measurements collected during a bioremediation experiment at a uranium‐contaminated site near Rifle, Colorado (USA). The SIP data were first inverted for Cole‐Cole parameters, including chargeability, time constant, resistivity at the DC frequency, and dependence factor, at each pixel of two‐dimensional grids using a previously developed stochastic method. Correlations between the inverted Cole‐Cole parameters and the wellbore‐based groundwater chemistry measurements indicative of key metabolic processes within the aquifer (e.g., ferrous iron, sulfate, uranium) were established and used as a basis for petrophysical model development. The developed Bayesian model consists of three levels of statistical submodels: (1) data model, providing links between geochemical and geophysical attributes, (2) process model, describing the spatial and temporal variability of geochemical properties in the subsurface system, and (3) parameter model, describing prior distributions of various parameters and initial conditions. The unknown parameters were estimated using Markov chain Monte Carlo methods. By combining the temporally distributed geochemical data with the spatially distributed geophysical data, we obtained the spatiotemporal distribution of ferrous iron, sulfate, and sulfide, and their associated uncertainty information. The obtained results can be used to assess the efficacy of the bioremediation treatment over space and time and to constrain reactive transport models.