  Seismic imaging in challenging sedimentary basins and reservoirs requires
acquiring, processing, and imaging very large volumes of data (tens of
terabytes). To reduce the cost of acquisition and the time from acquiring the
data to producing a subsurface image, novel acquisition systems based on
compressive sensing, low-rank matrix recovery, and randomized sampling have
been developed and implemented. These approaches allow practitioners to achieve
dense wavefield reconstruction from a substantially reduced number of field
samples.
  However, designing acquisition surveys suited for this new sampling paradigm
remains a critical and challenging role in oil, gas, and geothermal
exploration. Typical random designs studied in the low-rank matrix recovery and
compressive sensing literature are difficult to achieve by standard industry
hardware. For practical purposes, a compromise between stochastic and
realizable samples is needed. In this paper, we propose a deterministic and
computationally cheap tool to alleviate randomized acquisition design, prior to
survey deployment and large-scale optimization. We consider universal and
deterministic matrix completion results in the context of seismology, where a
bipartite graph representation of the source-receiver layout allows for the
respective spectral gap to act as a quality metric for wavefield
reconstruction. We provide realistic scenarios to demonstrate the utility of
the spectral gap as a flexible tool that can be incorporated into existing
survey design workflows for successful seismic data acquisition via low-rank
and sparse signal recovery.
