Bayesian theory used in designing the ocean floor electromagnetic sounding experiment

In the Bayesian formulation of inversion the information content for each unknown parameter is quantified in terms of its marginal posterior probability distribution, which defines the accuracy expected in inversion. The problem of seafloor electromagnetic sounding is defined in terms of recovering the electrical conductivity profile beneath the seafloor from measurements of the electromagnetic field. Electromagnetic inversion represents a strongly nonlinear problem for which a direct solution is not available. A matched-field approach to this problem can be formulated based on Bayesian inversion theory, which provides environmental parameter estimates and their uncertainties. This paper investigates the contribution of various experimental factors to the information content of the inversion parameters aiming to recover the conductivity profile from measurements of the electromagnetic field.