Optimizing an experimental design is a compromise between maximizing information we get about the target and limiting the cost of the experiment, providing a wide range of constraints. We present a statistical algorithm to design an electromagnetic experiment in the context of CO2 sequestration. This algorithm combines the use of linearized inverse theory (to quantify the quality of one given design) and genetic algorithm (to examine a wide range of possible surveys). The particularity of our algorithm is the use of a multi-objective genetic algorithm called NSGA II that searches designs that fit several objective functions simultaneously. We test our new algorithm with a realistic one-dimensional resistivity structure. Our first synthetic test shows that a limited number of observations, well distributed, have the potential to resolve the given model well, according to our criterion of quality. This synthetic test also points out the importance of a well chosen objective function, depending on our target. We then use our multi-objective algorithm to find a survey design that maximizes the information we get about the reservoir layer.


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