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Full-Bayesian inversion of the Edwards Aquifer.

Yefang Jiang1, Allan D Woodbury, Scott Painter

  • 1Department of Civil Engineering, University of Manitoba, 15 Gillson St., Winnipeg, Manitoba, Canada R3T 3V5. yfjiang@gov.pe.ca

Ground Water
|October 2, 2004
PubMed
Summary

This study extends a Bayesian inverse approach to estimate aquifer transmissivity fields, improving accuracy with hydraulic head data. The method offers a more effective calibration for groundwater flow models in heterogeneous aquifers.

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Area of Science:

  • Hydrogeology
  • Geophysics
  • Computational methods

Background:

  • Estimating transmissivity fields in heterogeneous aquifers is crucial for groundwater flow modeling.
  • Existing Bayesian inverse methods require enhancements for complex hydrogeological conditions.

Purpose of the Study:

  • To extend the Bayesian inverse approach for estimating transmissivity fields in highly heterogeneous aquifers.
  • To develop a linearized finite element formulation for improved accuracy and efficiency.

Main Methods:

  • A first-order Taylor series approximation is used for exponential terms in the governing equation.
  • A linear finite element formulation relates hydraulic head to logarithm of transmissivity (ln(T)) perturbations.
  • An updating procedure similar to Woodbury and Ulrych (2000) is implemented.

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Main Results:

  • The linearized solution achieved high accuracy (R2 = 0.96) in a generic test case.
  • Incorporating hydraulic head data significantly improved ln(T) estimates compared to sparse data interpolation.
  • Calibration of a MODFLOW model for the Edwards Aquifer showed improved head fit using the new Bayesian approach.

Conclusions:

  • The developed Bayesian inverse method effectively estimates transmissivity fields in complex aquifer systems.
  • The approach enhances groundwater model calibration by integrating hydraulic head data.
  • The method is adaptable for use with traditional MODFLOW grids, improving aquifer characterization.