Conditional Simulation: Practical Application for Sampling Design Optimization

Detailed spatial models generated by conditional simulation provide a powerful tool for case-specific optimization of sampling designs. The entire process of sampling, estimation, and decision can be simulated on such a model by a Monte-Carlo approach. Optimization can be based on economic functions or on decision quality constraints rather than simple minimization of estimation variance. Efficient algorithms and 32-bit desktop computers make simulation feasible for routine use. A design solution based on conditional simulation will approach the true optimum only to the degree that the simulations accurately reflect the relevant real-world characteristics. The quality of a simulation depends on a number of factors including the number of conditioning data, the accuracy of the variogram model, and the use of data transformations. The method is illustrated with two examples one based on the well-known Walker Lake model and the other on an actual case study involving remediation of contaminated soils. For practical purposes, the method appears to be accurate, precise, and robust.