Abstract

Seismic data processing is aimed at increasing the signal to noise ratio to produce an interpretable image of the subsurface. Raw shot gathers are generally contaminated by the presence of coherent and incoherent noise that masks the primary reflection events of interest for faithful and confident reconstruction of the subsurface. The buzzword is therefore to enhance the signal to noise ratio with approaches and methodologies that do not hurt the basic character of the seismic dataset. Various generic and conventional processing workflows in use do not assure in producing good image of the subsurface in presence of rough topography, near surface and subsurface velocity variations & complex geology among others, which happen to be the characteristic of the area under study. Therefore, selection of optimal set of processing parameters and judicious use of various noise removal schemes assumes primary significance for arriving at geologically conformable output sections.