Earth Sciences predictions are facing large uncertainty related to the complexity and the lack of knowledge of the subsurface. Acquiring the most informative data set to reduce uncertainty is therefore highly valuable. However, its identification rapidly becomes intractable for large scale
problems. We propose to stochastically solve this problem under large uncertainty using our newly developed Bayesian Evidential Learning framework.