Interpolating and Denoising seismic data by using least square of inverse Radon domain

Abstract

In seismic data processing and interpretation, we need to have a complete set of data with regular sampling along offset. Therefore, we need to reconstruct and interpolate seismic data at missing spatial locations. Interpolation of seismic data is considered as an inverse problem that we try to reconstruct seismic data at locations where measurements are not acquired, due to problems in seismic acquisition. We propose a new method for Interpolation of seismic data by using Radon domain. Radon domain has three advantages over other domains. First, there are not spatial or other kinds of aliasing in this domain. Second, seismic events are separated based on their dips. Last but not least, removing some traces does not change the Radon domain. The method entails solving an inverse problem that we invert Radon domain by using least square method and reconstruct seismic data at missing spatial locations. This new method is quite efficient and it has an ability to interpolate and denoise (Linear noise) seismic data simultaneously.