High-Resolution Radon Demultiple

Abstract

It is well known that conventional Radon demultiple may fail when there is limited differential moveout between primaries and multiples or when the input data contain aliased events. These limitations can be overcome by an extension of the conventional Radon transform which uses data-derived constraints to enhance the focusing of energy in the transform domain. This leads to better separation of primaries and multiples and an improved resistance to errors due to noise and aliasing. Data examples show the benefits that result in practice from this high-resolution version of the transform: a) Better multiple removal and signal preservation when primaries and multiples have small differential moveout: b) Removal of aliased multiples without the need for pre-interpolation: c) Removal of aliased noise in the course of demultiple processing.