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Due to the tremendous cost of seismic data acquisition, methods have beendeveloped to reduce the amount of data acquired by designing optimal missingtrace reconstruction algorithms. These technologies are designed to record aslittle data as possible in the field, while providing accurate wavefieldreconstruction in the areas of the survey that are not recorded. This isachieved by designing randomized subsampling masks that allow for accuratewavefield reconstruction via matrix completion methods. Motivated by theserecent results, we propose a simulation-free seismic survey design that aims atimproving the quality of a given randomized subsampling using a simulatedannealing algorithm that iteratively increases the spectral gap of thesubsampling mask, a property recently linked to the quality of thereconstruction. We demonstrate that our proposed method improves the datareconstruction quality for a fixed subsampling rate on a realistic syntheticdataset.

A simulation-free seismic survey design by maximizing the spectral gap