I applied a bi-objective optimization strategy to search the best seismic survey design in illumination and cost senses. Due to the conflicting goals of obtaining a good subsurface illumination at the lowest possible cost it is not possible to obtain an optimum survey in both senses simultaneously, but instead it is possible to get a set of surveys, called Pareto Front, that shows the trade-off between these conflicting objectives. As a result, the Pareto Front could be used as a decision tool to tune quality versus cost. I used the mixed-integer, free-derivative, nonlinear optimization algorithm called Particle Swarm Optimization and Mesh Adaptive Direct Search. The Particle Swarm Optimization part is used to escape local minima while the mixed-integer part is used to deal with integer aspects of a seismic survey design like the number of receivers and sources, to name but a few. I tested the optimization using a synthetic model and compared the final migrated seismic images. The results show good quality imaging and better cost.
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