Spectral Gap-Based Seismic Survey Design

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