Innovative Processing Approaches to Overcome Sampling Sparseness and Irregularity in 3D OBC Seismic Data Offshore Abu Dhabi

Abstract

Due mainly to commercial and operational constraints, seismic data are often sparsely and irregularly sampled, leading to several challenges in processing of 3D OBC seismic data offshore Abu Dhabi. Conventional linear noise attenuation techniques are not effective with Scholte waves as they are usually aliased with typical sampling interval in 3D OBC seismic data, and sometimes scattered because of near-surface heterogeneity. To address this, we apply model-based surface wave attenuation, Surface Wave Analysis Modeling and Inversion (SWAMI), which enables an estimate of local near-surface properties by analysing dispersion curves. Thus, both direct and scattered Scholte waves are effectively modelled and attenuated without suffering a lack of spatial sampling. We also highlight the shortcomings of the application of interferometry to scattered noise attenuation for sparse acquisition geometry. Matching Pursuit Fourier Interpolation (MPFI) is then implemented to deal with insufficient sampling in crossline direction caused by acquisition geometry. MPFI employs anti-aliasing capability so optimum data reconstruction can be performed for any frequency ranges. In addition to regularization aspect, MPFI with 5D implementation (4 spatial coordinates and time) is targeted to densify receiver line interval and extend source lines, which consequently enhances fold, offset and azimuth distributions of the data.