Seismic data processing: Current industry practice and new directions

Abstract

The last ten years have seen an evolution in the state of the art for seismic data processing on a number of fronts. Data transformations investigated have made some types of analyses much more straightforward. Deconvolution has become a sophisticated process which includes statistical, model‐based, and deterministic methods. Vibroseis® processing has led to a greater understanding of the statistical limitations in recovery of the wave‐field amplitude from sign‐bit recording, and the deconvolution of Vibroseis data has improved. Multichannel filtering and analysis in transform domains have resulted in increasingly effective tools for noise reduction and signal enhancement. In statics analysis, surface consistency as a constraint remains a standard, and refraction analysis has become popular as a means of preconditioning data for residual statics estimation. Advances in stacking methodology have come mainly from addressing three effects: ray bending through lateral velocity variations, complex structure, and source‐receiver azimuth variations. Recently many techniques of interpretation processing have been introduced with the goal of improving the estimate of earth properties, rather than generating a stacked or imaged section of higher fidelity. The interaction among related disciplines has increased and this serves to amplify the effectiveness of each discipline because they are now developing in less isolation.

Reprocessing 2-D airgun seismic reflection data SALTFLU (salt deformation and sub-salt fluid circulation in the Algero-Balearic abyssal plain) in the Balearic promontory and the Algerian basin

“…It is therefore essential to attenuate as much as possible the low frequency noise (e.g., reverberation from the direct arrival, 'swell' noise caused by pressure fluctuation near the sea-surface) beforehand, because any remaining low frequency noise not correlated with the source pulse may be artificially boosted by the source deconvolution and deghosting filters (Yilmaz and Baysal 2015). Thanks to lower computational costs in recent decades, multi-channel filtering and analysis in transform domains has become routine for noise reduction (Schultz 1985). For example, 'swell' noise can often be better attenuated by predictive filters in the frequency-space (F-X) domain than by a simple time domain low-cut filter that results in loss of the low frequency signal along with the attenuated noise (Liu and Goulty 1999;Schonewille et al 2008).…”

Reprocessed 2-D airgun seismic reflection data SALTFLU (salt deformation and sub-salt fluid circulation in the Algero-Balearic abyssal plain) in the Balearic promontory and the Algerian basin

“…It is therefore essential to attenuate as much as possible the low frequency noise (e.g., reverberation 90 from the direct arrival, 'swell' noise caused by pressure fluctuation near the sea-surface) beforehand, because any remaining low frequency noise not correlated with the source pulse may be artificially boosted by the source deconvolution and deghosting filters (Yilmaz and Baysal, 2015). Thanks to lower computational costs in recent decades, multi-channel filtering and analysis in transform domains has become routine for noise reduction (Schultz, 1985). For example, 'swell' noise can often be better attenuated by predictive filters in the frequency-space (F-X) 95 domain than by a simple time domain low-cut filter that results in loss of the low frequency signal along with the attenuated noise (Liu and Goulty, 1999;Schonewille et al, 2008).…”

Reprocessing 2-D Airgun Seismic Reflection Data SALTFLU (Salt Deformation And Sub-Salt Fluid Circulation In The Algero-Balearic Abyssal Plain) In the Balearic Promontory and the Algerian Basin

“…Thanks to lower computational costs in recent decades, multi-channel ltering and analysis in transform domains has become routine for noise reduction (Schultz, 1985). For example, 'swell' noise can often be better attenuated by predictive lters in the frequency-space (F-X) domain than by a simple time domain low-cut lter that results in loss of the low frequency signal along with the attenuated noise (Liu and Goulty, 1999;Schonewille et al, 2008).…”