Using the Nonstationary Spectral Method to Analyze Flow Through Heterogeneous Trending Media

This paper describes a nonstationary spectral theory for analyzing flow in a heterogeneous porous medium with a systematic trend in log hydraulic conductivity. This theory relies on a linearization of the groundwater flow equation but does not require the stationarity assumptions used in classical spectral theories. The nonstationary theory is illustrated with a two-dimensional analysis of a linear trend aligned with the mean flow direction. In this case, closed-form solutions can be obtained for the effective hydraulic conductivity, head covariance, and log conductivity-head cross covariance. The effective hydraulic conductivity decreases from the geometrical mean as the mean slope of the log conductivity increases. Trending leads to a reduction of head variance and a structural change in the head covariance and the log conductivity-head cross covariance. Such changes have important implications for measurement conditioning (or cokriging) methods which rely on the head covariance and log conductivity-head covariance. The nonstationary spectral analysis is also compared with classical spectral analysis. This comparison indicates that the classical spectral method correctly predicts the normalized head covariance in a linear trending media. The stationary spectral method fails to capture the qualitative influence of trends on the effective hydraulic conductivity and the log conductivity-head cross covariance, although the magnitude of the error is relatively small for realistic values of the mean log conductivity slope. The stationary and nonstationary results are the same when there is no trend in log conductivity. The trending conductivity example illustrates that the nonstationary spectral method has all the capabilities of the classical spectral approach while not requiring as many restrictive assumptions.

[1]  M. Mariño,et al.  Effective hydraulic conductivity of nonstationary aquifers , 1994 .

[2]  Dennis McLaughlin,et al.  A Stochastic Method for Characterizing Ground‐Water Contamination , 1993 .

[3]  M. A. Mariño,et al.  Stochastic groundwater flow analysis in the presence of trends in heterogeneous hydraulic conductivity fields , 1993 .

[4]  E. Eric Adams,et al.  Field study of dispersion in a heterogeneous aquifer , 1992 .

[5]  Michael A. Celia,et al.  Large-scale natural gradient tracer test in sand and gravel, Cape Cod, Massachusetts: 3. Hydraulic c , 1992 .

[6]  Dennis McLaughlin,et al.  A nonstationary spectral method for solving stochastic groundwater problems: unconditional analysis , 1991 .

[7]  Richard M. Jones Critical time for NSF, NASA funding bills , 1991 .

[8]  Harihar Rajaram Scale dependent dispersion in heterogeneous porous media , 1991 .

[9]  Dennis McLaughlin,et al.  Identification of large-scale spatial trends in hydrologic data. , 1990 .

[10]  Yoram Rubin,et al.  A stochastic approach to the problem of upscaling of conductivity in disordered media: Theory and unconditional numerical simulations , 1990 .

[11]  R. Ababou,et al.  Numerical simulation of three-dimensional saturated flow in randomly heterogeneous porous media , 1989 .

[12]  D. McLaughlin,et al.  Stochastic analysis of nonstationary subsurface solute transport: 2. Conditional moments , 1989 .

[13]  Maliha S. Nash,et al.  Soil physical properties at the Las Cruces trench site , 1989 .

[14]  Steven M. Gorelick,et al.  Effective groundwater model parameter values: Influence of spatial variability of hydraulic conductivity, leakance, and recharge , 1989 .

[15]  E. Wood,et al.  A distributed parameter approach for evaluating the accuracy of groundwater model predictions: 1. Theory , 1988 .

[16]  Dennis McLaughlin,et al.  A distributed parameter approach for evaluating the accuracy of groundwater model predictions: 2. Application to groundwater flow , 1988 .

[17]  R. Ababou,et al.  Three-dimensional flow in random porous media , 1988 .

[18]  A. Yaglom Basic Properties of Stationary Random Functions , 1987 .

[19]  A. Yaglom Correlation Theory of Stationary and Related Random Functions I: Basic Results , 1987 .

[20]  E. Sudicky A natural gradient experiment on solute transport in a sand aquifer: Spatial variability of hydraulic conductivity and its role in the dispersion process , 1986 .

[21]  Peter K. Kitanidis,et al.  Comparison of Gaussian Conditional Mean and Kriging Estimation in the Geostatistical Solution of the Inverse Problem , 1985 .

[22]  Peter K. Kitanidis,et al.  Analysis of the Spatial Structure of Properties of Selected Aquifers , 1985 .

[23]  S. P. Neuman,et al.  Analysis of nonintrinsic spatial variability by residual kriging with application to regional groundwater levels , 1984 .

[24]  C. Axness,et al.  Three‐dimensional stochastic analysis of macrodispersion in aquifers , 1983 .

[25]  Allan L. Gutjahr,et al.  Stochastic analysis of spatial variability in two‐dimensional steady groundwater flow assuming stationary and nonstationary heads , 1982 .

[26]  G. Dagan Stochastic modeling of groundwater flow by unconditional and conditional probabilities: 1. Conditional simulation and the direct problem , 1982 .

[27]  R. Allan Freeze,et al.  Stochastic analysis of steady state groundwater flow in a bounded domain: 2. Two‐dimensional simulations , 1979 .

[28]  Bruno O. Shubert,et al.  Random variables and stochastic processes , 1979 .

[29]  Allan L. Gutjahr,et al.  Stochastic analysis of spatial variability in subsurface flows: 1. Comparison of one‐ and three‐dimensional flows , 1978 .