This study applies a stacking-based data-driven signal-to-noise ratio (SNR) estimation approach to 3D synthetic and field data from the desert environment. This method delivers a volume of absolute SNR estimates, with each estimate requiring a local 3D window. Analysis of SNR volumes and slices provides valuable insight into acquisition geometries and processing steps. We suggest simple SNR bounds for reliable event tracking and amplitude interpretation on migrated volumes. We discover extremely low SNR in prestack field data down to -40 dB, significantly complicating processing effort. While acquisition density can significantly raise SNR of imaged post-stack data, it remains to be seen if time processing can robustly handle such a low SNR and lead to reliable prestack migrated data suitable for acoustic impedance inversion. We advocate the use of SNR volumes for quantitative assessment of prestack and post-stack data from in-field QC to processing. The stacking-based method is entirely data-driven and only requires approximate NMO velocities to generate SNR volumes. Avoiding human bias typical during qualitative data assessment by various experts and establishing data-driven metrics can be helpful for industry decisions and automatic evaluations.
