Imaging performance analysis of compressive sensing seismic exploration

In seismic exploration, the acquisition of ideal seismic data is often hindered by various constraints such as high costs and environmental limitations, leading to compromised imaging quality. Compressive sensing (CS) offers novel theories and tools to analyze and guide the acquisition and processing of seismic data. This paper explores the technical advantages and physical mechanisms of CS seismic exploration techniques in direct imaging of irregularly acquired data and imaging of reconstructed data compared to traditional acquisition methods. Our research findings demonstrate that irregular acquired data based on CS acquisition inherently possesses anti-aliasing capabilities in migration imaging, converting strong regular migration noise generated by regular sampling into noise with a random distribution, and through stacking processes suppress migration noise, thereby effectively enhancing the signal-to-noise ratio (SNR) of imaging. Furthermore, irregularly acquired data exhibits reconstruction advantages, enabling the reconstruction of regular high-density seismic data that yields superior imaging effects. Hence, CS seismic exploration technology is conducive to improving the imaging quality of seismic data.