  We address an optimal sensor placement problem through Bayesian experimental
design for seismic full waveform inversion for the recovery of the associated
moment tensor. The objective is that of optimally choosing the location of the
sensors (stations) from which to collect the observed data. The Shannon
expected information gain is used as the objective function to search for the
optimal network of sensors. A closed form for such objective is available due
to the linear structure of the forward problem, as well as the Gaussian
modeling of the observational errors and prior distribution. The resulting
problem being inherently combinatorial, a greedy algorithm is deployed to
sequentially select the sensor locations that form the best network for
learning the moment tensor. Numerical results are presented and analyzed under
several instances of the problem, including: use of full three-dimensional
velocity-models, cases in which the earthquake-source location is unknown, as
well as moment tensor inversion under model misspecification
