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A deterministic algorithm for experimental design applied to tomographic and microseismic monitoring surveys
SUMMARY
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
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