Abstract:
Most general experimental design algorithms are either: (i) stochastic and hence give different designs each time they are run with finite computing power, or (ii) deterministic but converge to results that depend on an initial or reference design, taking little or no account of the range of all other possible designs. In this paper we introduce an approximation to standard measures of experimental design quality that enables a new algorithm to be used. The algorithm is simple, deterministic and the resulting experimental design is influenced by the full range of possible designs, thus addressing problems (i) and (ii) above. Although the designs produced are not guaranteed to be globally optimal, they significantly increase the magnitude of small eigenvalues in the model-data relationship (without requiring that these eigenvalues be calculated). This reduces the model uncertainties expected post-experiment. We illustrate the method on simple tomographic and microseismic location examples with varying degrees of seismic attenuation.
