Efficient Seismic Data Acquisition Using 2-D Deep Compressed Sensing

Despite the importance of seismic surveys, acquiring seismic data remains a costly endeavor. Compressed sensing (CS) offers a means to significantly reduce acquisition costs, particularly when implemented through deep CS networks (DCSNs). However, applying CS to seismic data introduces several challenges, some of which have received limited attention. One key issue is that the CS reconstruction process tends to favor samples with high amplitudes. Since the signal gain in seismic data decays exponentially over time, the reconstruction quality correspondingly degrades, resulting in irregular reconstruction performance across the time axis. In this work, three contributions are presented to address these issues. First, the aforementioned degradation problem is mitigated by modifying the loss function to incorporate gain-corrected data, thereby enabling more uniform reconstruction quality over time. Second, a 2-D sampling scheme is deployed, which compresses the data along both the temporal and spatial dimensions to further reduce acquisition costs. Finally, the effectiveness of the proposed cost function and sampling scheme is demonstrated using variants of DCSNs originally designed for natural images. The results show significant improvements in the reconstruction performance of late-time samples and confirm that seismic data can be accurately recovered from a small subset of measurements, thereby reducing overall acquisition costs.