Dealing with spatial 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 based on multi-channel filters are not effective with Scholte waves as they are usually aliased with typical sampling intervals in 3D OBC seismic data (e.g., 25 m source and receiver point intervals), and sometimes scattered because of near-surface heterogeneity. To address them, we apply model-based surface wave attenuation, Surface Wave Analysis Modelling 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 from under-sampling. A data interpolation and regularization technique called Matching Pursuit Fourier Interpolation (MPFI) is then applied to enhance spatial sampling. MPFI employs an anti-aliasing capability so optimum data reconstruction can be performed for any frequency range. In addition to the regularization aspect, MPFI with a 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.The implementation of the two techniques successfully addresses processing challenges in sparsely and irregularly sampled OBC seismic data.