Abstract

A new method for fully nonlinear, Bayesian survey design renders the optimization of industrial-scale geoscientific surveys as a practical possibility. The method, DN optimization, designs surveys to maximally discriminate between different possible models. It is based on a generalization to nonlinear design problems of the D criterion (which is for linearized design problems). The main practical advantage of DN optimization is that it uses efficient algorithms developed for linearized design theory, resulting in lower computing and storage costs than other nonlinear Bayesian design techniques. In a real example in which we optimized a seafloor microseismic sensor network to monitor a fractured petroleum reservoir, we compared DN optimization with two other networks: one proposed by an industrial contractor and one optimized using a linearized Bayesian design method. Our technique yielded a network with superior expected data quality in terms of reduced uncertainties on hypocenter locations.

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