No abstract

No abstract

No abstract

No abstract

Interbed multiples have always presented a big challenge to interpreters of land seismic data, especially in Middle East basins where impedance contrasts can be quite high and appear at different levels of the subsurface. These interbed multiples not only affect the stack image, making the structural interpretation very uncertain, but also the pre-stack data, making the extraction of reliable AVO and azimuthal AVO attributes at the contaminated interfaces extremely difficult, if not impossible. Attempts at tackling these interbed multiples on conventional data using deconvolution techniques, 1D modelling or moveout-based methods are common practices (Berkhout & Verschuur, 1999; Jakubowicz 1998; Alvarez & Larne, 2004); while these methods can improve the stack image, their impact on the AVO response is often limited, especially on noisy land seismic. Recently, we are seeing more and more high density surveys being acquired thanks to new acquisition techniques such as simultaneous sourcing acquisition (Rozemond, 1996; Bouska, 2008; Howe et al., 2008); it has already been shown how this new generation of dataset can have a huge impact on the quality of the processing products and pre-stack attributes (Ourabah et al, 2014 & 2015). In this study, we demonstrate how we can also use high density dataset to remove interbed multiples by applying an innovative workflow to the Rumaila high density field trial dataset.