The Impact of Surface-Wave Separation on Seismic Survey Design

Abstract

Abstract 3-D seismic survey design should provide an acquisition geometry that enables imaging and amplitude-versus-offset applications of target reflectors with sufficient data quality under given economical and operational constraints. However, in land or shallow water environments, surface waves are often dominant in the seismic data. The effectiveness of surface-wave separation or removal significantly affects the quality of the final result. Therefore, the need for surface-wave separation imposes additional constraints on the acquisition geometry. Recently, we proposed a method for surface-wave separation that can better deal with aliased seismic data than classic filtering methods such as in the wavenumber-frequency domain. This will impact survey design. Here, we investigate how the choice of a particular surface-wave separation affects the selection of survey parameters and the resulting data quality. To quantify the latter, we introduce a measure that represents the estimated signal-to-noise ratio between the desired subsurface signal and the surface waves that are deemed to be noise. In a case study, we applied surface-wave separation and signal-to-noise ratio estimation to several data sets with different survey parameters. The spatial sampling intervals of the basic subset are the survey parameters that affect the performance of surface-wave separation methods the most. Finer spatial sampling will reduce aliasing and make surface-wave separation easier, resulting in better data quality until no further improvement is obtained. We observed this behaviour in our results as a main trend that levels off at increasingly denser sampling. With our new method, this trend curve lies at a considerably higher signal-to-noise ratio than with a classic filtering method. This means that we can obtain a much better data quality for given survey effort, or the same data quality as with a conventional method at lower cost.