Numerical models that solve governing equations for subsurface fluid flow and transport are
commonly applied to analyze quantitatively the effects of heterogeneity. These models …

Abstract When Water Resources Research was launched in 1965, heterogeneity,
uncertainty, and scale issues in subsurface hydrology were in the backburner. Only about 10 …

Information theory and inference, often taught separately, are here united in one entertaining
textbook. These topics lie at the heart of many exciting areas of contemporary science and …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Prediction of a random field based on observations of the random field at some set of
locations arises in mining, hydrology, atmospheric sciences, and geography. Kriging, a …

Conventional geostatistical methodology solves the problem of predicting the realized value
of a linear functional of a Gaussian spatial stochastic process S (x) based on observations …

Risk and reliability analysis is an area of growing importance in geotechnical engineering,
where many variables have to be considered. Statistics, reliability modeling and engineering …

Feedforward neural networks such as multilayer perceptrons are popular tools for nonlinear
regression and classi cation problems. From a Bayesian perspective, a choice of a neural …

Understanding spatial statistics requires tools from applied and mathematical statistics,
linear model theory, regression, time series, and stochastic processes. It also requires a …

Engineers and applied geophysicists routinely encounter interpolation and estimation
problems when analysing data from field observations. Introduction to Geostatistics presents …