
%0 Journal Article
%A Hendrick, Natasha 
%A Hearn, Steve 
%T Introduction to vector-processing techniques for multi-component seismic exploration
%J ASEG Extended Abstracts
%D 2003/08/01
%V 2003
%N 2
%P 1-5
%K 
%R https://doi.org/10.1071/ASEG2003AB071
%U https://www.publish.csiro.au/paper/ASEG2003AB071 
%X Conventional multi-component seismic analysis simply relies on appropriate component selection to provide P- and S-wave images. However, this ignores the potential cross-contamination of P-wave energy on the horizontal components, and S-wave energy on the vertical component that may occur in certain geological situations. Where wavefield cross-contamination occurs, there is potential to achieve cleaner P- and S-wave images by more fully exploiting the true vector nature of multi-component seismic data. Vector processing for exploration-scale data typically combines frequency and slowness information, together with particle motion, to distinguish different wave types. Three such multi-trace, multi-component wavefield separation schemes, termed MUSIC, IWSA and PIM, are considered here. These vector techniques all utilise a parametric approach whereby wavefield slowness and polarisation are modelled simultaneously in the frequency domain. The PIM algorithm is considered to be the most generally useful of the three algorithms. Synthetic and ocean-bottom data examples are used to demonstrate practical issues relating to the use of these vector separation schemes. In cases where there is significant cross-contamination, vector wavefield separation produces P- and S-wave records that differ significantly from the vertical and horizontal components, respectively. Where cross-contamination is less problematic, production vector processing is not warranted. In these cases, however, vector processing still provides valuable quantitative validation of the natural-separation assumption. 
