Empirical Bayes regionalization methods for spatial stochastic processes
- Gradient Corp., Cambridge, MA (United States)
- Duke Univ., Durham, NC (United States)
- Ambiotec, Harlingen, TX (United States)
Many geophysical properties can be described as spatial stochastic processes, including spatially correlated hydraulic conductivity fields. Use of regional data can potentially improve estimation of such processes. The authors consider the case in which observations at each of several sites are described by a general linear model, while the parameters of these models arise from a common regional distribution. Parametric empirical Bayes methods enable the determination of the parameters of the regional distribution via maximum likelihood. However, such methods have not been utilized for spatial stochastic processes. They develop the application of a simple iterative technique for maximum likelihood estimation of the regional parameters, and demonstrate its use with a common parameterization of the spatial covariance structure. Synthetic data tests show the potential for substantial reduction in estimation risk through use of such techniques.
- OSTI ID:
- 5945814
- Journal Information:
- Water Resources Research; (United States), Vol. 27:1; ISSN 0043-1397
- Country of Publication:
- United States
- Language:
- English
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58 GEOSCIENCES
GROUND WATER
FLOW MODELS
ENVIRONMENTAL TRANSPORT
EXPECTATION VALUE
FLUID FLOW
HYDRAULIC CONDUCTIVITY
HYDROLOGY
PARAMETRIC ANALYSIS
RISK ASSESSMENT
STATISTICAL MODELS
STOCHASTIC PROCESSES
HYDROGEN COMPOUNDS
MASS TRANSFER
MATHEMATICAL MODELS
OXYGEN COMPOUNDS
WATER
540310* - Environment
Aquatic- Basic Studies- (1990-)
580000 - Geosciences