Bayesian survey design for maximal waveform inversion resolution

We describe a Bayesian methodology for designing seismic experiments that optimally maximize model parameter resolution for waveform imaging purposes. The proposed optimal experiment design (OED) algorithm finds the measurements which are likely to optimally reduce the expected uncertainty on the model parameters. This Bayesian D-optimality-based algorithm minimizes the volume of the expected confidence ellipsoid and leads to the maximization of the expected resolution of the model parameters. Computational efficiency is achieved by a greedy algorithm in which the design is sequentially improved. The benefits of the proposed method over traditional non-Bayesian ones are demonstrated with several geophysical examples. These include reducing large seismic data volumes for real-time imaging and solving the problem of designing seismic surveys that account for source bandwidth, signal-to-noise ratio and attenuation.