  For estimation and predictions of random fields it is increasingly
acknowledged that the kriging variance may be a poor representative of true
uncertainty. Experimental designs based on more elaborate criteria that are
appropriate for empirical kriging are then often non-space-filling and very
costly to determine. In this paper, we investigate the possibility of using a
compound criterion inspired by an equivalence theorem type relation to build
designs quasi-optimal for the empirical kriging variance, when space-filling
designs become unsuitable. Two algorithms are proposed, one relying on
stochastic optimization to explicitly identify the Pareto front, while the
second uses the surrogate criteria as local heuristic to chose the points at
which the (costly) true Empirical Kriging variance is effectively computed. We
illustrate the performance of the algorithms presented on both a simple
simulated example and a real oceanographic dataset.
