Advanced signal processing tools for dispersive waves

ABSTRACT Two field examples are presented, showing the advantages of using multicomponent sensors for surface‐wave studies. Multicomponent sensors allow the use of specific signal‐processing tools such as the multicomponent singular value decomposition filter and the multicomponent polarization filter, which are both very efficient at separating surface waves from the other waves that comprise a seismic field record. Firstly, some signal‐processing tools for studying surface waves are described. The various filters range from classical to advanced techniques. For processing single‐component data, the filters are the f−k filter and filters based on singular value decomposition and on spectral matrix decomposition. For processing multicomponent data, the filters are the 4C‐singular value decomposition filter and the classical or high‐order polarization filter. Secondly, processing sequences that can be applied to the field data are described and the single‐component processing sequence and the multicomponent processing sequence are compared. Two field examples are presented. The first data set is a land seismic data recording on 2C sensors. The second data set was obtained from a marine acquisition with OBS (4 components). The results obtained illustrate the advantages of using multicomponent filters. The efficiency of the 4C‐SVD filter and the high‐order statistic polarization filter is demonstrated.