Introduction to Conditional Simulation

Conditional simulation is a method used to create multiple, equally probable, realisations of a spatially distributed variable, given a set of known data points. This technique is based on a random function theory, which allows the integration of spatial variability and the uncertainty associated with the prediction of spatially related data.

Conditional simulation allows you to assess the variability of your input data and measure the likelihood of the desired outcome (risk). This varies from an estimation in that estimations do not reproduce the variability of the data and do not allow you to analyse the uncertainty or risk involved.

Conditional simulation results vary depending on the amount, distribution and quality of the data available in a spatial area. In areas that are well understood, with lots of data, similar simulation models are generally produced and there is lower risk. However, in regions that are not well understood and do not have good data coverage, simulation models are more variable and are therefore, higher risk.

Note: This tutorial specifically covers univariate (standard) conditional simulations, not multivariate simulations.

This activity guides you through setting up your project for conditional simulation in Supervisor, inserting and configuring the simulations, and assessing some of the results.

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