Attenuation of receiver ghosts in variable-depth streamer high-resolution seismic reflection data

• Published in: Marine geophysical researches Vol. 41; no. 2
• Main Authors: Provenzano, Giuseppe, Henstock, Timothy J., Bull, Jonathan M., Bayrakci, Gaye
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2020; Springer Nature B.V
• Subjects: Algorithms; Attenuation; Damping; Data; Depth; Earth and Environmental Science; Earth Sciences; Geophysics/Geodesy; Oceanography; Offshore Engineering; Original Research Paper; Periodicity; Polls & surveys; Reflection; Resolution; Sea surface; Surveying; Signal processing; Seismic reflection data; High-resolution geophysics; Inverse problems; Seismic resolution
• ISSN: 0025-3235; 1573-0581
• DOI: 10.1007/s11001-020-09407-9

Receiver ghosts attenuate marine seismic reflection data at harmonic frequencies that depend on the propagation angle and the streamer depth below the sea surface. The resulting loss of bandwidth is one of the major factors hampering seismic resolution. In near-surface and legacy multi-channel data, receivers depth is often unknown and may vary significantly both along the streamer length and during the survey, making frequency–slowness deghosting techniques unsuitable. In this work, we present a method for the attenuation of receiver ghost reflections in data with an arbitrary streamer depth profile varying during the survey. For each trace, a different deghosting operator is estimated and applied at different two-way-time windows, in order to account for depth-dependent changes in reflection angles. The ghost null frequencies are picked on the time-varying power spectrum via an automatic algorithm, guided by a user-dependent a priori function, and optimised to respect the harmonics’ periodicity. The power of the inverse filter is adjusted by adaptively damping abnormal amplitudes in the deghosted spectra. The algorithm is applied to high-resolution (GI-gun, 20–400 Hz) and ultra-high-resolution (Sparker, 0.2–3.0 kHz) multi-channel datasets, yielding an excellent bandwidth recovery and gain in resolution in the final stacks. Limited computing time and straightforward application make the method widely applicable and cost-effective.