Multimeasurement aliased-noise attenuation for sparse land seismic acquisition using compressed sensing

Abstract

Land data are contaminated by energetic surface waves that have low apparent velocities relative to the seismic signal. The conventional land acquisition approach to noise attenuation is to deploy arrays of hard-wired geophones whose combined response acts as a spatial filter. Besides the shortcomings of a fixed array response that is sensitive to perturbations, using a large amount of equipment limits the field efficiency of array-based acquisition. The advent of point-receiver acquisition technology addresses some of these limitations by enabling measurement of each single geophone along a receiver line and removing the slowest energetic noise by velocity-based filtering. However, according to the classic sampling theorem, the proper sampling of noise requires sampling the shortest wavelength twice. Because surface waves have slow propagation velocities, it is not feasible to sample the noise properly and a compromise is made between quality and efficiency. We have developed two different methods to improve the efficiency of point-receiver technology by allowing for wider receiver spacing without compromising the coherent noise attenuation capabilities. First, we combine compressed sensing-based methods such as the matching pursuit approach with the gradient measurement inferred from a novel 5C sensor to dealias and attenuate source-generated noise. Second, we have developed a method to design optimized acquisition geometries by using the spectral characteristics of the surface waves, where the objective is to place a limited number of receivers at the most favorable locations to help attenuate the coherent noise. We determine the effectiveness of our methods through synthetic and field data examples in which we find that spatially aliased coherent noise can be reconstructed to a denser grid and attenuated.