Abstract

Seismic data processing aims at generating interpretable subsurface images which mimic the geology of the study area. These subsurface images are an aggregate of the quality of the raw data acquired in the field and the algorithms used to process them. The quality of the raw data depends on the acquisition constraints as well as the geology of the area. In areas with severe energy penetration issues due to the presence of boulder beds or loose formation in the near surface, reflections are either masked by high amplitude energy blasts in the near offsets and are very feeble in the deeper portion. Thus, segregation of signal from noise is very difficult even after several attempts of noise attenuation and surface consistent amplitude balancing. In this type of scenario, methodologies which will help in delineating the subsurface structures by balancing these high amplitudes and improving the stacking power of the signal become a necessity in seismic data processing.