In this workbook, we provide an understanding of the philosophy and implementation of parameter estimation and geostatistics in EVS. EVS's capabilities to grid and interpolate sparse measured data in three dimensions are unparalleled. EVS performs most of its interpolation using an accurate and geostatistically defensible process called Kriging. Kriging is a mathematical process recognized by the EPA as the best and standard means for interpolation and extrapolation of measured data. EVS provides a user-friendly expert system to drive its Kriging modules, lifting the burden of determining optimal semivariogram parameters from the user. With EVS, the user can rely on expert system calculated default values to provide quality answers in minimal time.
Kriging is the only data estimation method that also provides statistical measures of goodness. When data is analyzed in EVS, the user is also provided statistical confidence and an additional parameter that we call uncertainty. These additional statistical measures are extremely useful in guiding future site investigation efforts. Our experience is that by using maximum uncertainty to guide site investigations, a 30% reduction (or more) in sampling locations can be realized for an equivalent quality of assessment.
As a user defined alternative to confidence and uncertainty, EVS will provide statistical bounds on the parameter estimate (This feature is only available in EVS PRO). In other words, EVS will determine the nominal, minimum, and maximum estimated distribution based on a user specified confidence level. With EVS, you can now directly answer the question: With my limited measured data, to an 80% confidence level, what is the largest and smallest plume I can expect?
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