A GEOSTATISTICAL APPROACH TO CONTAMINANT SOURCE IDENTIFICATION

A geostatistical approach to contaminant source estimation is presente d. The problem is to estimate the release history of a conservative so lute given point concentration measurements at some time after the rel ease. A Bayesian framework is followed to derive the best estimate and to quantify the estimation error. The relation between this approach and common regularization and interpolation schemes is discussed. The performance of the method is demonstrated for transport in a simple on e-dimensional homogeneous medium, although the approach is directly ap plicable to transport in two- or three-dimensional domains. The method ology produces a best estimate of the release history and a confidence interval. Conditional realizations of the release history are generat ed that are useful in visualization and risk assessment. The performan ce of the method with sparse data and large measurement error is exami ned. Emphasis is placed on formulating the estimation method in a comp utationally efficient manner. The method does not require the inversio n of matrices whose size depends on the grid size used to resolve the solute release history. The issue of model validation is addressed.