# Estimation

A longstanding and important problem in geological mapping and modeling is the calculation of estimates at unsampled locations. Kriging is widely used in geostatistics for this purpose. The central idea is to calculate an estimate that minimizes the expected squared error between the unknown true value and the estimate. In general, all data that are related to the unsampled location should have an opportunity to influence the estimate.

A central feature of geostatistics from the early days in the 1960s is that estimation must consider a site-specific variogram model of spatial correlation that accounts for the spatial structure of the variable being estimated including anisotropy and other detailed features. Another central feature of estimation is that the estimates should be constructed with a clearly defined measure of optimality. In practice, an optimal estimate is one that minimizes the estimation variance. Lessons here are focused on the decisions required in the construction of optimal estimates.

# Lessons

- Introduction to Choosing a Kriging Plan
- An Overview of Multiple Indicator Kriging
- Signed Distance Function Modeling with Multiple Categories
- Assessing Metrics of Kriging Performance
- The Multivariate Gaussian Distribution for Local Uncertainty
- Setup of Multiple Indicator Kriging
- Variants of Local Stationarity
- Mapping Compositional Geochemical Measurements
- Workflows for Kriging with Locally Varying Anisotropy
- Cokriging in Presence of Unequally Sampled Data
- Mapping of Geographic Variables
- Implementation of Bayesian Maximum Entropy
- Discrete Spline Interpolation

- When a Trend is Required and How to Model a Trend
- Correct Discretization for Different Model Applications
*Managing Outliers*- Modeling Tabular Deposits
- Implementation of Variants of Colocated Cokriging