  Due to the tremendous cost of seismic data acquisition, methods have been
developed to reduce the amount of data acquired by designing optimal missing
trace reconstruction algorithms. These technologies are designed to record as
little data as possible in the field, while providing accurate wavefield
reconstruction in the areas of the survey that are not recorded. This is
achieved by designing randomized subsampling masks that allow for accurate
wavefield reconstruction via matrix completion methods. Motivated by these
recent results, we propose a simulation-free seismic survey design that aims at
improving the quality of a given randomized subsampling using a simulated
annealing algorithm that iteratively increases the spectral gap of the
subsampling mask, a property recently linked to the quality of the
reconstruction. We demonstrate that our proposed method improves the data
reconstruction quality for a fixed subsampling rate on a realistic synthetic
dataset.
