GMDSI - J. Doherty - Basic Geostatistics - Part 1

This is the first of a two-part series. It discusses correlated random variables. It shows how knowledge of one such variable conditions estimation of the other, and reduces its uncertainty. These principles are then applied to regionalized random variables to demonstrate the concepts behind random parameter field generation and kriging. The semivariogram, and its relationship to the spatial covariance function, are also discussed.
Production of these videos was funded by GMDSI (Groundwater Modelling Decision Support Initiative)

[@lucasluzzi]

Thanks a lot. You made it understandable

[@ppatrocluss]

spectacular!

[@lucasluzzi]

How does that joint gaussian distribution in the beggining and the conditional expectation of one variable based on the other relate to kriging estimation?

[@symple-francescalotti5730]

The equations that PEST uses to estimate parameters can be re-formulated as conditioning equations that pertain to a Gaussian distribution. For PEST, one conditions k (parameters) by h (observations). When kriging one conditions k at one place by k at other places. In the first case covariances between h and k are calculated by the model (assumed to be linear). In the second case covariances are embodied in C(k), the covariance matrix between parameters that is derived from a variogram.
I hope that this (at least partially) answers your question.
Best wishes
John