Data Fusion Modeling for Groundwater Systems using Generalized Kalman Filtering
Engineering projects involving groundwater systems are faced with uncertainties because the earth is heterogeneous and data sets are fragmented. Current methods providing support for management decisions are limited by the data types, models, computations, or simplifications. Data fusion modeling (DFM) removes many of the limitations and provides predictive modeling to help close the management control loop. DFM is a spatial and temporal state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities and for temporal variation in driving terms. Kalman filtering methods are generalized by introducing information filtering methods due to Bierman coupled with (1) a Markov random field representation for spatial variation and (2) the representer method for transient dynamics from physical oceanography. DFM provides benefits for waste management, water supply, and geotechnical applications.
KeywordsClay Porosity Anisotropy Covariance Assimilation
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- G. J. Bierman and D.W. Porter, “Decentralized tracking via new square root information filter (SRIF) concepts,” Business and Technological Systems BTS-34–87–32, 1988.Google Scholar
- R. Chellappa and A. Jain Markov Random Fields: Theory and Application Academic Press, Boston, MA, 1991.Google Scholar
- J. J. Clark and A. L. Yuille Data Fusion for Sensory Information Processing Systems Kluwer, Boston, MA, 1990.Google Scholar
- P. Courtier, J. Derber, R. Errico, J. E Louis, and T. Vukicevic, “Important literature on the use of adjoint, variational methods and the Kalman filter in meteorology,” Tellus vol. 45A, no. 2, pp. 342–357, 1993.Google Scholar
- D. L. Hall Mathematical Techniques in Multisensor Data Fusion Artech House, Boston, MA, 1992.Google Scholar
- M. C. Hill, “A computer program (MODFLOWP) for estimating parameters of a transient, three-dimensional, ground-water flow model using nonlinear regression,” U.S. Geological Survey Report 91–484,1992.Google Scholar
- A. G. Journal and C. J. Huijbregts Mining Geostatistics Academic Press, New York, 1991.Google Scholar
- L. J. Levy and D. W. Porter, “Large-scale system performance prediction with confidence from limited field testing using parameter identification,” Johns Hopkins APL Technical Digest vol. 13, no. 2, pp. 300–308, 1992.Google Scholar
- D. McLaughlin and L. R. Townley, “A reassessment of the groundwater inverse problem,” Water Resources Research vol. 32, no. 5, pp. pp. 1131–1161, May 1996.Google Scholar
- D. W. Porter, “Quantitative data fusion: A distributed/parallel approach to surveillance, tracking, and navigation using information filtering,” Proc. Fifth Joint Service Data Fusion Symposium Johns Hopkins University/Applied Physics Laboratory, Laurel, MD, Oct. 1991.Google Scholar
- W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery Numerical Recipes in FORTRAN: The Art of Scientific Computing Cambridge University Press, New York, 1992.Google Scholar
- J. S. Vandergraft, “Efficient optimization methods for maximum likelihood parameter estimation,” Proc. 24th Conference on Decision and Control Ft. Lauderdale, FL, 1985.Google Scholar