Abstract Compressive sensing (CS) has emerged as a transformative approach for seismic acquisition, enabling sparser sampling while maintaining data quality. Although 1D CS designs along shot or receiver lines are well-suited for traditional surveys with large line spacing, modern acquisitions, such as ocean-bottom node (OBN) and high-density land surveys, require multidimensional optimization to mitigate spatial aliasing and improve cost efficiency. In this study, two novel applications of CS in seismic were introduced: (1) a 2D CS survey design framework leveraging mutual coherence (MC) maps to optimize source/receiver sampling in inline and crossline directions and (2) a legacy data bin-reduction method that uses MC maps to assist in reconstruction of high-resolution volumes from irregular historical acquisitions. The 2D CS framework was validated using SEG Advanced Modeling Corporation Phase II (SEAM II) synthetic data and an OBN field survey, demonstrating that it achieves about 5 dB higher signal-to-noise ratio (SNR) than 1D CS designs at equivalent sampling or reduces source/receiver requirements by 20% for comparable results. For legacy data, the MC maps provided a quantitative metric to assess reconstructability, enabling safe bin reduction to 75% of the original bin size while preserving the SNR, as demonstrated on a North American land data set. These advancements help bridge the gap between theoretical CS and field applications, offering practical guidelines for survey design and reprocessing. The MC map’s dual role as a design optimizer for new acquisitions and a quality-control tool for legacy data establishes it as a unifying framework for compressive seismic across the data lifecycle.
Jiang et al. (Sun,) studied this question.