Multi-component seismic wavefield separation via spectral matrix filtering

Abstract

Spectral matrix filtering (SMF) is a vector-processing method that uses frequency, slowness and particle- motion information to distinguish between wave types. The algorithm operates in the f-xdomain and exploits the eigenstructure of the frequency-domain covariance matrix of the multi-component seismic signal. SMF has previously been demonstrated on VSP data. A number of practical considerations for the application of SMF to surface-reflection data are considered. Practical application of SMF requires the user to specify a number of data-dependent parameters, including trace-width and time extent of the data-analysis window, and the frequency bandwidth of the filter. Synthetic and real-data examples illustrate that SMF can successfully extract high-amplitude wavefields while preserving other wave types in a surface-seismic record. Thus, while extensive testing suggests that SMF is not viable for separating P and PS reflection wavefields, SMF has the potential to routinely suppress groundroll energy and other high-amplitude surface-related events that can mask desirable reflection energy.